{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "sns.set_style({'font.sans-serif':['simhei','Arial']})\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "lianjia_df = pd.read_csv(r'C:\\Users\\Mr.You\\Desktop\\y\\lianjia\\items.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Direction</th>\n",
       "      <th>District</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Id</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Price</th>\n",
       "      <th>Region</th>\n",
       "      <th>Renovation</th>\n",
       "      <th>Size</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>郫县城区</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共32层)</td>\n",
       "      <td>106101180538</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>78.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>70.34㎡</td>\n",
       "      <td>未知年建</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>南湖</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共34层)</td>\n",
       "      <td>106101185900</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>140.0</td>\n",
       "      <td>东</td>\n",
       "      <td>其他</td>\n",
       "      <td>76.48㎡</td>\n",
       "      <td>2010年建</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>水碾河</td>\n",
       "      <td>无</td>\n",
       "      <td>高楼层 (共6层)</td>\n",
       "      <td>106101278157</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>72.0</td>\n",
       "      <td>西南</td>\n",
       "      <td>简装</td>\n",
       "      <td>65.49㎡</td>\n",
       "      <td>1995年建</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>成外</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共18层)</td>\n",
       "      <td>106100865898</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>130.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>114.48㎡</td>\n",
       "      <td>2011年建</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>万象城</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共33层)</td>\n",
       "      <td>106100851902</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>179.0</td>\n",
       "      <td>南</td>\n",
       "      <td>简装</td>\n",
       "      <td>80.54㎡</td>\n",
       "      <td>2012年建</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Direction District Elevator       Floor            Id Layout  Price Region  \\\n",
       "0        NaN     郫县城区        有  中楼层 (共32层)  106101180538   2室2厅   78.0     东南   \n",
       "1        NaN       南湖        有  低楼层 (共34层)  106101185900   2室1厅  140.0      东   \n",
       "2        NaN      水碾河        无   高楼层 (共6层)  106101278157   2室1厅   72.0     西南   \n",
       "3        NaN       成外        有  低楼层 (共18层)  106100865898   3室1厅  130.0     东南   \n",
       "4        NaN      万象城        有  中楼层 (共33层)  106100851902   2室1厅  179.0      南   \n",
       "\n",
       "  Renovation     Size    Year  \n",
       "0         其他   70.34㎡    未知年建  \n",
       "1         其他   76.48㎡  2010年建  \n",
       "2         简装   65.49㎡  1995年建  \n",
       "3         其他  114.48㎡  2011年建  \n",
       "4         简装   80.54㎡  2012年建  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lianjia_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2998 entries, 0 to 2997\n",
      "Data columns (total 11 columns):\n",
      "Direction     0 non-null float64\n",
      "District      2998 non-null object\n",
      "Elevator      2991 non-null object\n",
      "Floor         2998 non-null object\n",
      "Id            2998 non-null int64\n",
      "Layout        2998 non-null object\n",
      "Price         2998 non-null float64\n",
      "Region        2998 non-null object\n",
      "Renovation    2998 non-null object\n",
      "Size          2998 non-null object\n",
      "Year          2998 non-null object\n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 257.7+ KB\n"
     ]
    }
   ],
   "source": [
    "lianjia_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lianjia_df = lianjia_df.drop(columns=['Direction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        70.34㎡\n",
       "1        76.48㎡\n",
       "2        65.49㎡\n",
       "3       114.48㎡\n",
       "4        80.54㎡\n",
       "5       121.92㎡\n",
       "6        67.28㎡\n",
       "7       189.38㎡\n",
       "8       122.79㎡\n",
       "9        84.77㎡\n",
       "10      254.22㎡\n",
       "11       62.91㎡\n",
       "12      119.02㎡\n",
       "13       76.49㎡\n",
       "14      122.49㎡\n",
       "15      125.82㎡\n",
       "16       48.13㎡\n",
       "17       50.08㎡\n",
       "18        83.6㎡\n",
       "19      137.64㎡\n",
       "20      129.42㎡\n",
       "21       86.85㎡\n",
       "22      186.17㎡\n",
       "23      143.03㎡\n",
       "24        92.3㎡\n",
       "25         130㎡\n",
       "26       116.2㎡\n",
       "27       89.05㎡\n",
       "28      115.73㎡\n",
       "29       93.86㎡\n",
       "         ...   \n",
       "2968     75.85㎡\n",
       "2969     78.53㎡\n",
       "2970    113.02㎡\n",
       "2971    124.54㎡\n",
       "2972    273.91㎡\n",
       "2973      88.1㎡\n",
       "2974      82.3㎡\n",
       "2975       135㎡\n",
       "2976     80.11㎡\n",
       "2977     88.21㎡\n",
       "2978     68.37㎡\n",
       "2979    178.76㎡\n",
       "2980     86.97㎡\n",
       "2981     92.41㎡\n",
       "2982     90.43㎡\n",
       "2983     56.09㎡\n",
       "2984    131.58㎡\n",
       "2985     63.98㎡\n",
       "2986    116.76㎡\n",
       "2987    147.21㎡\n",
       "2988    140.88㎡\n",
       "2989       108㎡\n",
       "2990        76㎡\n",
       "2991     97.36㎡\n",
       "2992    157.13㎡\n",
       "2993     85.23㎡\n",
       "2994     69.29㎡\n",
       "2995     83.84㎡\n",
       "2996     89.84㎡\n",
       "2997     90.04㎡\n",
       "Name: Size, Length: 2998, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lianjia_df['Size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "lianjia_df['Year'] = lianjia_df['Year'].str.extract('(\\d+)年建', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "lianjia_df['Size'] = lianjia_df.Size.apply(lambda x: eval(x.split('㎡')[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>District</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Id</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Price</th>\n",
       "      <th>Region</th>\n",
       "      <th>Renovation</th>\n",
       "      <th>Size</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>郫县城区</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共32层)</td>\n",
       "      <td>106101180538</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>78.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>70.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>南湖</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共34层)</td>\n",
       "      <td>106101185900</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>140.0</td>\n",
       "      <td>东</td>\n",
       "      <td>其他</td>\n",
       "      <td>76.48</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>水碾河</td>\n",
       "      <td>无</td>\n",
       "      <td>高楼层 (共6层)</td>\n",
       "      <td>106101278157</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>72.0</td>\n",
       "      <td>西南</td>\n",
       "      <td>简装</td>\n",
       "      <td>65.49</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>成外</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共18层)</td>\n",
       "      <td>106100865898</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>130.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>114.48</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>万象城</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共33层)</td>\n",
       "      <td>106100851902</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>179.0</td>\n",
       "      <td>南</td>\n",
       "      <td>简装</td>\n",
       "      <td>80.54</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>成外</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共16层)</td>\n",
       "      <td>106100858321</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>179.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>精装</td>\n",
       "      <td>121.92</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>衣冠庙</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共15层)</td>\n",
       "      <td>106101346654</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>117.0</td>\n",
       "      <td>西南</td>\n",
       "      <td>简装</td>\n",
       "      <td>67.28</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>营门口</td>\n",
       "      <td>无</td>\n",
       "      <td>高楼层 (共6层)</td>\n",
       "      <td>106100829238</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>235.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>189.38</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>红牌楼</td>\n",
       "      <td>无</td>\n",
       "      <td>低楼层 (共7层)</td>\n",
       "      <td>106101344236</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>216.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>122.79</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>光华大道沿线</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共34层)</td>\n",
       "      <td>106101216837</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>115.0</td>\n",
       "      <td>南</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>84.77</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>金融城</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共30层)</td>\n",
       "      <td>106100847600</td>\n",
       "      <td>5室2厅</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>南 北</td>\n",
       "      <td>其他</td>\n",
       "      <td>254.22</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>龙泉驿城区</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共21层)</td>\n",
       "      <td>106101205772</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>70.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>62.91</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>蓝谷地</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共11层)</td>\n",
       "      <td>106101204724</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>195.0</td>\n",
       "      <td>南</td>\n",
       "      <td>其他</td>\n",
       "      <td>119.02</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>大源</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共31层)</td>\n",
       "      <td>106101199820</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>200.0</td>\n",
       "      <td>南</td>\n",
       "      <td>其他</td>\n",
       "      <td>76.49</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>建设路</td>\n",
       "      <td>无</td>\n",
       "      <td>低楼层 (共7层)</td>\n",
       "      <td>106101439797</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>232.0</td>\n",
       "      <td>东</td>\n",
       "      <td>简装</td>\n",
       "      <td>122.49</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>广都</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共18层)</td>\n",
       "      <td>106101358713</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>190.0</td>\n",
       "      <td>北</td>\n",
       "      <td>其他</td>\n",
       "      <td>125.82</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>橡树湾</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共33层)</td>\n",
       "      <td>106101584779</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>60.0</td>\n",
       "      <td>南 北</td>\n",
       "      <td>简装</td>\n",
       "      <td>48.13</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>驷马桥</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共21层)</td>\n",
       "      <td>106101148150</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>90.0</td>\n",
       "      <td>东</td>\n",
       "      <td>其他</td>\n",
       "      <td>50.08</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>一品天下</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共28层)</td>\n",
       "      <td>106101212590</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>240.0</td>\n",
       "      <td>东北</td>\n",
       "      <td>其他</td>\n",
       "      <td>83.60</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>紫荆</td>\n",
       "      <td>无</td>\n",
       "      <td>高楼层 (共6层)</td>\n",
       "      <td>106101211969</td>\n",
       "      <td>4室3厅</td>\n",
       "      <td>203.0</td>\n",
       "      <td>南</td>\n",
       "      <td>其他</td>\n",
       "      <td>137.64</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>温江老城</td>\n",
       "      <td>暂无数据</td>\n",
       "      <td>低楼层 (共7层)</td>\n",
       "      <td>106101210160</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>117.0</td>\n",
       "      <td>南 北</td>\n",
       "      <td>其他</td>\n",
       "      <td>129.42</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>三圣乡</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共34层)</td>\n",
       "      <td>106101208079</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>157.0</td>\n",
       "      <td>西南</td>\n",
       "      <td>其他</td>\n",
       "      <td>86.85</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>猛追湾</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共22层)</td>\n",
       "      <td>106101202470</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>370.0</td>\n",
       "      <td>西北</td>\n",
       "      <td>其他</td>\n",
       "      <td>186.17</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>中和</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共26层)</td>\n",
       "      <td>106101193052</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>278.0</td>\n",
       "      <td>北</td>\n",
       "      <td>其他</td>\n",
       "      <td>143.03</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>沙湾</td>\n",
       "      <td>无</td>\n",
       "      <td>中楼层 (共7层)</td>\n",
       "      <td>106101191167</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>120.0</td>\n",
       "      <td>东南 西北</td>\n",
       "      <td>其他</td>\n",
       "      <td>92.30</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>华阳</td>\n",
       "      <td>无</td>\n",
       "      <td>高楼层 (共6层)</td>\n",
       "      <td>106101194420</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>200.0</td>\n",
       "      <td>南</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>130.00</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>万象城</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共18层)</td>\n",
       "      <td>106101190098</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>225.0</td>\n",
       "      <td>东</td>\n",
       "      <td>精装</td>\n",
       "      <td>116.20</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>大源</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共31层)</td>\n",
       "      <td>106101189807</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>249.0</td>\n",
       "      <td>东</td>\n",
       "      <td>精装</td>\n",
       "      <td>89.05</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>成外</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共25层)</td>\n",
       "      <td>106101189657</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>200.0</td>\n",
       "      <td>东北</td>\n",
       "      <td>其他</td>\n",
       "      <td>115.73</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>橡树湾</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共25层)</td>\n",
       "      <td>106101188679</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>93.0</td>\n",
       "      <td>东北</td>\n",
       "      <td>其他</td>\n",
       "      <td>93.86</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>茶店子</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共30层)</td>\n",
       "      <td>106101278839</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>155.0</td>\n",
       "      <td>西南</td>\n",
       "      <td>其他</td>\n",
       "      <td>75.85</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2969</th>\n",
       "      <td>武侯立交</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共11层)</td>\n",
       "      <td>106101457774</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>92.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>78.53</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2970</th>\n",
       "      <td>新双楠</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共16层)</td>\n",
       "      <td>106101250622</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>256.0</td>\n",
       "      <td>东</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>113.02</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>南湖</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共26层)</td>\n",
       "      <td>106100836035</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>170.0</td>\n",
       "      <td>西</td>\n",
       "      <td>其他</td>\n",
       "      <td>124.54</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2972</th>\n",
       "      <td>外双楠</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共11层)</td>\n",
       "      <td>106100144918</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>700.0</td>\n",
       "      <td>西北</td>\n",
       "      <td>精装</td>\n",
       "      <td>273.91</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2973</th>\n",
       "      <td>东升镇</td>\n",
       "      <td>暂无数据</td>\n",
       "      <td>低楼层 (共6层)</td>\n",
       "      <td>106101421352</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>63.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>88.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2974</th>\n",
       "      <td>东光小区</td>\n",
       "      <td>无</td>\n",
       "      <td>中楼层 (共6层)</td>\n",
       "      <td>106101320937</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>110.0</td>\n",
       "      <td>南</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>82.30</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>东苑</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共26层)</td>\n",
       "      <td>106101083043</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>248.0</td>\n",
       "      <td>西南</td>\n",
       "      <td>简装</td>\n",
       "      <td>135.00</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>外光华</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共21层)</td>\n",
       "      <td>106101394087</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>165.0</td>\n",
       "      <td>西</td>\n",
       "      <td>其他</td>\n",
       "      <td>80.11</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>人民公园</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共32层)</td>\n",
       "      <td>106101394986</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>205.0</td>\n",
       "      <td>西北</td>\n",
       "      <td>简装</td>\n",
       "      <td>88.21</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2978</th>\n",
       "      <td>驷马桥</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共21层)</td>\n",
       "      <td>106101331679</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>110.0</td>\n",
       "      <td>东</td>\n",
       "      <td>其他</td>\n",
       "      <td>68.37</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979</th>\n",
       "      <td>南湖</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共34层)</td>\n",
       "      <td>106101139552</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>270.0</td>\n",
       "      <td>西南</td>\n",
       "      <td>其他</td>\n",
       "      <td>178.76</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2980</th>\n",
       "      <td>国宾</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共30层)</td>\n",
       "      <td>106100993027</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>144.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>86.97</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2981</th>\n",
       "      <td>国宾</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共26层)</td>\n",
       "      <td>106101295507</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>157.0</td>\n",
       "      <td>西南</td>\n",
       "      <td>精装</td>\n",
       "      <td>92.41</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>南湖</td>\n",
       "      <td>无</td>\n",
       "      <td>低楼层 (共6层)</td>\n",
       "      <td>106101202087</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>92.0</td>\n",
       "      <td>南 北</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>90.43</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>高升桥</td>\n",
       "      <td>无</td>\n",
       "      <td>低楼层 (共7层)</td>\n",
       "      <td>106101372702</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>69.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>茶店子</td>\n",
       "      <td>无</td>\n",
       "      <td>中楼层 (共7层)</td>\n",
       "      <td>106101243610</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>180.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>131.58</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2985</th>\n",
       "      <td>蜀汉路</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共12层)</td>\n",
       "      <td>106101151693</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>102.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>63.98</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>驷马桥</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共18层)</td>\n",
       "      <td>106100959171</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>152.0</td>\n",
       "      <td>东 东北</td>\n",
       "      <td>简装</td>\n",
       "      <td>116.76</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>草金立交</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共11层)</td>\n",
       "      <td>106101431917</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>200.0</td>\n",
       "      <td>南</td>\n",
       "      <td>精装</td>\n",
       "      <td>147.21</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>大源</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共31层)</td>\n",
       "      <td>106101624048</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>355.0</td>\n",
       "      <td>西</td>\n",
       "      <td>其他</td>\n",
       "      <td>140.88</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989</th>\n",
       "      <td>大源</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共29层)</td>\n",
       "      <td>106101250467</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>225.0</td>\n",
       "      <td>东</td>\n",
       "      <td>其他</td>\n",
       "      <td>108.00</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>外双楠</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共9层)</td>\n",
       "      <td>106101335262</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>128.0</td>\n",
       "      <td>南</td>\n",
       "      <td>简装</td>\n",
       "      <td>76.00</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2991</th>\n",
       "      <td>成渝立交</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共33层)</td>\n",
       "      <td>106101192442</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>182.0</td>\n",
       "      <td>东</td>\n",
       "      <td>其他</td>\n",
       "      <td>97.36</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>华阳</td>\n",
       "      <td>无</td>\n",
       "      <td>低楼层 (共6层)</td>\n",
       "      <td>106101158820</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>230.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>157.13</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>成外</td>\n",
       "      <td>有</td>\n",
       "      <td>中楼层 (共18层)</td>\n",
       "      <td>106101147030</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>110.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>其他</td>\n",
       "      <td>85.23</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>万象城</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共28层)</td>\n",
       "      <td>106101117504</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>125.0</td>\n",
       "      <td>东北</td>\n",
       "      <td>简装</td>\n",
       "      <td>69.29</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>神仙树</td>\n",
       "      <td>无</td>\n",
       "      <td>高楼层 (共7层)</td>\n",
       "      <td>106101088215</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>92.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>83.84</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>万象城</td>\n",
       "      <td>有</td>\n",
       "      <td>高楼层 (共32层)</td>\n",
       "      <td>106101064187</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>199.0</td>\n",
       "      <td>东南</td>\n",
       "      <td>简装</td>\n",
       "      <td>89.84</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>东郊记忆</td>\n",
       "      <td>有</td>\n",
       "      <td>低楼层 (共33层)</td>\n",
       "      <td>106101096033</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>158.0</td>\n",
       "      <td>东北</td>\n",
       "      <td>精装</td>\n",
       "      <td>90.04</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2998 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     District Elevator       Floor            Id Layout   Price Region  \\\n",
       "0        郫县城区        有  中楼层 (共32层)  106101180538   2室2厅    78.0     东南   \n",
       "1          南湖        有  低楼层 (共34层)  106101185900   2室1厅   140.0      东   \n",
       "2         水碾河        无   高楼层 (共6层)  106101278157   2室1厅    72.0     西南   \n",
       "3          成外        有  低楼层 (共18层)  106100865898   3室1厅   130.0     东南   \n",
       "4         万象城        有  中楼层 (共33层)  106100851902   2室1厅   179.0      南   \n",
       "5          成外        有  高楼层 (共16层)  106100858321   3室2厅   179.0     东南   \n",
       "6         衣冠庙        有  低楼层 (共15层)  106101346654   2室1厅   117.0     西南   \n",
       "7         营门口        无   高楼层 (共6层)  106100829238   4室2厅   235.0     东南   \n",
       "8         红牌楼        无   低楼层 (共7层)  106101344236   3室2厅   216.0     东南   \n",
       "9      光华大道沿线        有  低楼层 (共34层)  106101216837   3室1厅   115.0      南   \n",
       "10        金融城        有  高楼层 (共30层)  106100847600   5室2厅  1100.0    南 北   \n",
       "11      龙泉驿城区        有  高楼层 (共21层)  106101205772   2室1厅    70.0     东南   \n",
       "12        蓝谷地        有  高楼层 (共11层)  106101204724   3室2厅   195.0      南   \n",
       "13         大源        有  低楼层 (共31层)  106101199820   2室2厅   200.0      南   \n",
       "14        建设路        无   低楼层 (共7层)  106101439797   3室2厅   232.0      东   \n",
       "15         广都        有  中楼层 (共18层)  106101358713   3室2厅   190.0      北   \n",
       "16        橡树湾        有  高楼层 (共33层)  106101584779   2室2厅    60.0    南 北   \n",
       "17        驷马桥        有  低楼层 (共21层)  106101148150   1室1厅    90.0      东   \n",
       "18       一品天下        有  高楼层 (共28层)  106101212590   3室1厅   240.0     东北   \n",
       "19         紫荆        无   高楼层 (共6层)  106101211969   4室3厅   203.0      南   \n",
       "20       温江老城     暂无数据   低楼层 (共7层)  106101210160   3室2厅   117.0    南 北   \n",
       "21        三圣乡        有  高楼层 (共34层)  106101208079   2室2厅   157.0     西南   \n",
       "22        猛追湾        有  低楼层 (共22层)  106101202470   4室2厅   370.0     西北   \n",
       "23         中和        有  中楼层 (共26层)  106101193052   4室2厅   278.0      北   \n",
       "24         沙湾        无   中楼层 (共7层)  106101191167   2室2厅   120.0  东南 西北   \n",
       "25         华阳        无   高楼层 (共6层)  106101194420   3室1厅   200.0      南   \n",
       "26        万象城        有  低楼层 (共18层)  106101190098   3室2厅   225.0      东   \n",
       "27         大源        有  高楼层 (共31层)  106101189807   2室2厅   249.0      东   \n",
       "28         成外        有  中楼层 (共25层)  106101189657   4室2厅   200.0     东北   \n",
       "29        橡树湾        有  低楼层 (共25层)  106101188679   2室1厅    93.0     东北   \n",
       "...       ...      ...         ...           ...    ...     ...    ...   \n",
       "2968      茶店子        有  低楼层 (共30层)  106101278839   2室2厅   155.0     西南   \n",
       "2969     武侯立交        有  低楼层 (共11层)  106101457774   2室2厅    92.0     东南   \n",
       "2970      新双楠        有  中楼层 (共16层)  106101250622   3室2厅   256.0      东   \n",
       "2971       南湖        有  中楼层 (共26层)  106100836035   4室2厅   170.0      西   \n",
       "2972      外双楠        有  低楼层 (共11层)  106100144918   4室2厅   700.0     西北   \n",
       "2973      东升镇     暂无数据   低楼层 (共6层)  106101421352   2室2厅    63.0     东南   \n",
       "2974     东光小区        无   中楼层 (共6层)  106101320937   3室1厅   110.0      南   \n",
       "2975       东苑        有  低楼层 (共26层)  106101083043   3室2厅   248.0     西南   \n",
       "2976      外光华        有  中楼层 (共21层)  106101394087   3室2厅   165.0      西   \n",
       "2977     人民公园        有  高楼层 (共32层)  106101394986   2室2厅   205.0     西北   \n",
       "2978      驷马桥        有  高楼层 (共21层)  106101331679   2室2厅   110.0      东   \n",
       "2979       南湖        有  高楼层 (共34层)  106101139552   4室2厅   270.0     西南   \n",
       "2980       国宾        有  低楼层 (共30层)  106100993027   2室1厅   144.0     东南   \n",
       "2981       国宾        有  低楼层 (共26层)  106101295507   3室2厅   157.0     西南   \n",
       "2982       南湖        无   低楼层 (共6层)  106101202087   2室1厅    92.0    南 北   \n",
       "2983      高升桥        无   低楼层 (共7层)  106101372702   2室1厅    69.0     东南   \n",
       "2984      茶店子        无   中楼层 (共7层)  106101243610   3室2厅   180.0     东南   \n",
       "2985      蜀汉路        有  高楼层 (共12层)  106101151693   2室1厅   102.0     东南   \n",
       "2986      驷马桥        有  中楼层 (共18层)  106100959171   3室2厅   152.0   东 东北   \n",
       "2987     草金立交        有  低楼层 (共11层)  106101431917   4室2厅   200.0      南   \n",
       "2988       大源        有  高楼层 (共31层)  106101624048   4室2厅   355.0      西   \n",
       "2989       大源        有  高楼层 (共29层)  106101250467   3室1厅   225.0      东   \n",
       "2990      外双楠        有   高楼层 (共9层)  106101335262   2室1厅   128.0      南   \n",
       "2991     成渝立交        有  中楼层 (共33层)  106101192442   3室2厅   182.0      东   \n",
       "2992       华阳        无   低楼层 (共6层)  106101158820   4室2厅   230.0     东南   \n",
       "2993       成外        有  中楼层 (共18层)  106101147030   2室2厅   110.0     东南   \n",
       "2994      万象城        有  高楼层 (共28层)  106101117504   2室1厅   125.0     东北   \n",
       "2995      神仙树        无   高楼层 (共7层)  106101088215   3室1厅    92.0     东南   \n",
       "2996      万象城        有  高楼层 (共32层)  106101064187   3室2厅   199.0     东南   \n",
       "2997     东郊记忆        有  低楼层 (共33层)  106101096033   3室2厅   158.0     东北   \n",
       "\n",
       "     Renovation    Size  Year  \n",
       "0            其他   70.34   NaN  \n",
       "1            其他   76.48  2010  \n",
       "2            简装   65.49  1995  \n",
       "3            其他  114.48  2011  \n",
       "4            简装   80.54  2012  \n",
       "5            精装  121.92  2011  \n",
       "6            简装   67.28  2005  \n",
       "7            其他  189.38  2000  \n",
       "8            其他  122.79  2003  \n",
       "9            毛坯   84.77  2014  \n",
       "10           其他  254.22  2013  \n",
       "11           其他   62.91  2005  \n",
       "12           其他  119.02  2006  \n",
       "13           其他   76.49  2014  \n",
       "14           简装  122.49  2002  \n",
       "15           其他  125.82  2007  \n",
       "16           简装   48.13  2013  \n",
       "17           其他   50.08  2016  \n",
       "18           其他   83.60  2012  \n",
       "19           其他  137.64  2002  \n",
       "20           其他  129.42  2006  \n",
       "21           其他   86.85  2009  \n",
       "22           其他  186.17  2000  \n",
       "23           其他  143.03  2013  \n",
       "24           其他   92.30  2000  \n",
       "25           毛坯  130.00  2010  \n",
       "26           精装  116.20  2008  \n",
       "27           精装   89.05  2014  \n",
       "28           其他  115.73  2012  \n",
       "29           其他   93.86  2016  \n",
       "...         ...     ...   ...  \n",
       "2968         其他   75.85  2009  \n",
       "2969         毛坯   78.53  2004  \n",
       "2970         毛坯  113.02  2011  \n",
       "2971         其他  124.54  2009  \n",
       "2972         精装  273.91  2006  \n",
       "2973         毛坯   88.10   NaN  \n",
       "2974         毛坯   82.30  2003  \n",
       "2975         简装  135.00  2009  \n",
       "2976         其他   80.11  2013  \n",
       "2977         简装   88.21  2009  \n",
       "2978         其他   68.37  2010  \n",
       "2979         其他  178.76  2010  \n",
       "2980         毛坯   86.97  2013  \n",
       "2981         精装   92.41  2011  \n",
       "2982         毛坯   90.43  2002  \n",
       "2983         其他   56.09  2000  \n",
       "2984         其他  131.58  1999  \n",
       "2985         其他   63.98  2006  \n",
       "2986         简装  116.76  2010  \n",
       "2987         精装  147.21  2005  \n",
       "2988         其他  140.88  2012  \n",
       "2989         其他  108.00  2014  \n",
       "2990         简装   76.00  2003  \n",
       "2991         其他   97.36  2008  \n",
       "2992         其他  157.13  2003  \n",
       "2993         其他   85.23  2011  \n",
       "2994         简装   69.29  2009  \n",
       "2995         毛坯   83.84  1995  \n",
       "2996         简装   89.84  2009  \n",
       "2997         精装   90.04  2009  \n",
       "\n",
       "[2998 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lianjia_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Price</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.998000e+03</td>\n",
       "      <td>2998.000000</td>\n",
       "      <td>2998.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.061012e+11</td>\n",
       "      <td>183.751167</td>\n",
       "      <td>102.417265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.085858e+05</td>\n",
       "      <td>115.257488</td>\n",
       "      <td>39.509790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.060923e+11</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>22.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.061011e+11</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>78.942500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.061012e+11</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>89.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.061014e+11</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>122.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.061017e+11</td>\n",
       "      <td>1298.000000</td>\n",
       "      <td>452.050000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Id        Price         Size\n",
       "count  2.998000e+03  2998.000000  2998.000000\n",
       "mean   1.061012e+11   183.751167   102.417265\n",
       "std    4.085858e+05   115.257488    39.509790\n",
       "min    1.060923e+11    30.000000    22.670000\n",
       "25%    1.061011e+11   115.000000    78.942500\n",
       "50%    1.061012e+11   155.000000    89.770000\n",
       "75%    1.061014e+11   210.000000   122.320000\n",
       "max    1.061017e+11  1298.000000   452.050000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lianjia_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = lianjia_df.copy()\n",
    "df['PerPrice'] = lianjia_df['Price']/lianjia_df['Size']\n",
    "\n",
    "# 重新摆放列位置\n",
    "columns = ['Region', 'District', 'Layout', 'Floor', 'Year', 'Size', 'Elevator', 'Renovation', 'PerPrice', 'Price']\n",
    "df = pd.DataFrame(df, columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Onl6WWUyRtOyoTDa2JrgeA3ydhybDxjON4vrwVB4cwKSTiXrojXkr8IvMvKN83uL3KZK+/2or9xTgT50qyMzXU2zHB5SJ23WBVwDPodgPun23sW06c7wCmXlfRPyMIuF6TGuTbf+SmRcD/xERm2fmWE/Mh/QujYgnAH9uH6hlHMsD0yJiBYrE3x3AD1rmT6dtvUfEU4FTKG45P6WM7dIoBo+5uizzSOBxwByK54y2+o+yznkUAydtnpl/LntOrl3eHn5b+d3/E9iBzgMGSZIkPYRJR0mSNOVExNMpBmHZo+wlOIfiVuvDgecC+2TmaRHxG4rnO86nSLydX1axHHBUh56Os8oXmfnnCUKYBTy1XKaTY9s+r09x6/PlwFatzxVsVY5k/DSKBND7IuKsLEaGXh54TjkQSS9mAU/OzCvokAzrV0S8nqJH6TrArROU24Di9vEXlQmsI4ArKHp4EsWo0SdQ9KrcjiJx2KsETqa4vfqNPQ52slzbv50rzvxih8krtP3bWv6yLu2uQpFInFFux4nMpNjOG1Gs373aBqlZoZzf2v68iNguM9tHxL645eMzKNbXRRTrvNXJFD07D2rb38+nuIX+ry3T7gTe1MszRyVJkgCix0feSJIkTRoRsQrw0sz8Vsu0lYFPAp/IzBtapq9H0cvxj5l5ZDlteYrroIfcwtxj+ysBq7a208My62TmdT2WnQaskpm39xFbUCQaF9adIIqIPShusf0V8D8T1R8Rz8zM8yJia2AX4GOtibeIeBXFgDSXZub549UzTt0rZuY93Uuq3B9WLp9zKkmS1BiTjpIkSZIkSZJq5ejVkiRJkiRJkmo1JZ/pePvtt9s9U5IkSZIkSRqR2bNnx0TzR9LTMSJWj4idImKNUbQvSZIkSZIkaXgaTzpGxGrAzyhG0jsrItbsUGZGRPwtIuaWr82bjlOSJEmSJElSf0Zxe/UWwHsz8/wyAfkU4PQOZb6XmQc1Hp0kSZIkSZKkgTTe0zEzf10mHJ9N0dvxvA7FtgF2i4jfRcRRETElnz0pSZIkSZIkPRyNJJkXEQHsC9wKLOxQ5EJgx8y8ISKOAXYBftqprvnz5w8tTkmSJEmSJEmFOXPm9Fw2Mkc3EHREfAy4PDN/0DZ9ucy8v3z/TmBmZn52bL6jV0uSJEmSJEmjM+lGr46IgyLiNeXHRwC3dSj2nYjYMiKmA3sClzQWoCRJkiRJkqSBNJ50BL4OvDoizgamA/+IiMPayhwKfAe4GDgvM89oOEZJkiRJkiRJfRrp7dX98vZqSZIkSZIkaXQm3e3VkiRJkiRJkpZtJh0lSZIkSZIk1WrGqAOoy44v2GUk7Z5x+qkjaVeSJEmSJEmarOzpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRamXSUJEmSJEmSVCuTjpIkSZIkSZJqZdJRkiRJkiRJUq1MOkqSJEmSJEmqlUlHSZIkSZIkSbUy6ShJkiRJkiSpViYdJUmSJEmSJNXKpKMkSZIkSZKkWpl0lCRJkiRJklQrk46SJEmSJEmSamXSUZIkSZIkSVKtTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUaSdIyI1SNip4hYYxTtS5IkSZIkSRqexpOOEbEa8DPgGcBZEbHmOOWOiojzIuLgRgOUJEmSJEmSNJBR9HTcAnhvZn4cOB14SnuBiHgJMD0znwlsEBFzGo5RkiRJkiRJUp9mNN1gZv4aICKeTdHb8dAOxbYHji/f/wLYFpjfqb758ztObsyo25ckSZIkSZKaMGdO7/0CG086AkREAPsCtwILOxRZCbiufP9vOvSGHFPlyw7DqNuXJEmSJEmSJpuRDCSThbcBlwIv6lDkLmCF8v3KOMq2JEmSJEmSNGWMYiCZgyLiNeXHRwC3dSg2j+KWaoAtgWsaCE2SJEmSJElSDUbRg/DrwKsj4mxgOvCPiDisrcyJZZkjgH2AUxqOUZIkSZIkSVKfRjGQzK3ATm2TD24rc0dEbF+WOzwzb28oPEmSJEmSJEkDGslAMr0ok5PHdy0oSZIkSZIkaVJxgBZJkiRJkiRJtTLpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRamXSUJEmSJEmSVCuTjpIkSZIkSZJqZdJRkiRJkiRJUq1MOkqSJEmSJEmqlUlHSZIkSZIkSbUy6ShJkiRJkiSpViYdJUmSJEmSJNXKpKMkSZIkSZKkWpl0lCRJkiRJklQrk46SJEmSJEmSamXSUZIkSZIkSVKtTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUqxmjDmBZt9PuLxlJu788+YSRtCtJkiRJkiTZ01GSJEmSJElSrUw6SpIkSZIkSaqVSUdJkiRJkiRJtWr8mY4RMRv4PjAduBvYNzMXtJWZAfylfAG8IzMvazRQSZIkSZIkSX0ZRU/HVwJHZObzgRuBnTuU2QL4XmZuX75MOEqSJEmSJElTROM9HTPzKy0f1wRu7lBsG2C3iHgucBmwf2Yu6lTf/Pnz6w+yglG3P57JGpckSZIkSZKmpjlz5vRctvGk45iIeCawWmae32H2hcCOmXlDRBwD7AL8tFM9Vb7sMIy6/fFM1rgkSZIkSZK07BtJ0jEiVge+COw1TpFLM/P+8v3vATNokiRJkiRJ0hTR+DMdI2IW8EPg/Zl57TjFvhMRW0bEdGBP4JLGApQkSZIkSZI0kFH0dHwD8BTggxHxQeAsYGZmHtxS5lDgOCCAn2bmGc2HKUmSJEmSJKkfoxhI5kjgyC5lLqcYwVqSJEmSJEnSFNP47dWSJEmSJEmSlm0mHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSaqVSUdJkiRJkiRJtTLpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRamXSUJEmSJEmSVCuTjpIkSZIkSZJqZdJRkiRJkiRJUq1MOkqSJEmSJEmqlUlHSZIkSZIkSbUaKOkYEc+rKxBJkiRJkiRJy4a+k44RsRdwRI2xSJIkSZIkSVoGzOhnoYjYEPg0cGBEHAYsaCuyXGZ+cNDgNDwvePHLRtLu6T/5/kjalSRJkiRJUnMqJR0jYhqwNfAlYB/gFmAtYFFrMWC5ugLUw8vO+7xuJO2edvy3R9KuJEmSJEnSsqhqT8djgJ2Bp2XmNeW0r9UakSRJkiRJkqQprWrS8d3Au4AfRcT+wE3AK4DrgZuByzPz+npDlCRJkiRJkjSVVBpIJjNvycwPAa8F/hfYHrgPWAfYETgxIk6IiDXrDlSSJEmSJEnS1FD1mY4zMnNRZl4REbsDZwI7ZuZfW8q8FzgZ2KbeUCVJkiRJkiRNBVVvrz49Iq4HvkxxS/X+wJKIeFxLmR8DF9cUnyRJkiRJkqQppmrS8cXAnsAXgacAfwauoRixeszMst4za4hPmhRe+Mr9R9Luz7/rOE2SJEmSJGnqqZR0zMw7KEawPiYi9gU+BfwsM7/Sax0RMRv4PjAduBvYNzMXdCh3FLApcEpmHlYlTkmSJEmSJEmjU2kgmVaZ+QPgP4BzKy76SuCIzHw+cCOwc3uBiHgJMD0znwlsEBFz+o1TkiRJkiRJUrOq3l69lMy8Abhh7HNE7JiZZ3RZprVX5JrAzR2KbQ8cX77/BbAtML9TffPnd5zcmFG3Px7jqsa4JEmSJEmSJjZnTu/9AgdKOnZwFLBeLwUj4pnAapl5fofZKwHXle//TfH8yI6qfNlhGHX74zGuaoxLkiRJkiSpPn0lHSPibOBJYx+BJZn5SODeHpdfnWIwmr3GKXIXsEL5fmUGuA1ckiRJkiRJUrMqJfMi4u0R8QRg5cxcHbg6M1cDLi2LZA91zAJ+CLw/M68dp9g8iluqAbakGCFbkiRJkiRJ0hRQtafjo4C5wOzyc7b924s3UNwu/cGI+CBwFjAzMw9uKXMi8JuIeAzwQmCbinFKkiRJkiRJGpFKScfMPDgiPgZcEhGvB9Yo/310+e+qEfEGiiTiV8ep40jgyC7t3BER2wM7AYdn5u1V4pQkSZIkSZI0OpWf6ZiZ90fEvcC6wHLlv8uX/86iGEhmxUEDy8xbeXAEa0mSJEmSJElTRKWkY0TsBrwPiMz8aETsWv77nPLfl2Xmh4cTqqR2u7zuXSNp99Rvf34k7UqSJEmSpKmh6qjQTwJeAyyJiFWB6eW/fY2CLUmSJEmSJGnZU/WZjp8CiIjrgPOBxcB5LfVUGVBGkiRJkiRJ0jKorx6Kmbn7OLNWGCAWScuA3fb7r5G1/bNvHj7uvN3f8qEGI1nayUd+bNx5L3rnYQ1GsrSffuHgkbUtSZIkSVq21X1b9G411ydJGoE9/t9/j6ztkz57wMjaliRJkiTVo+ozHZcSEdMiYu+WSfdFxJoDxiRJkiRJkiRpCqucdIyI1Vs+Tgc+2fL5PcD49xFKkiRJkiRJWuZVur06IuYAF0bER4AvZebCiLi7nPdoYF/gafWHKUkSvPigL46s7Z98+h3jznvJwd9oMJKlnXDYG0fWtiRJkiSNp+ro1fMjYhfgMOBFwPOAJRExC/g+cHBmXlt/mJIkqaq9Pnr0yNr+8UdeO7K2JUmSJI1e5YFkMvNcYIeIWLectDpwJnBsZn6tzuAkSdKyZ+9P/GBkbf/oA/uOrG1JkiTp4aTq7dWHAQtaPgfwOGAesFZEfLicNSszD64tSkmSpCHb5/ATR9b28f+157jz9v3caQ1GsrQfvGfnkbUtSZKkqa1qT8eZwG3AwpZpCdxSTo9y2qzBQ5MkSdJk9fIvnTWytr/39ueOrG1JkiT1puozHQ8aex8Ra2fmTRHxfmBbipGw35mZ99YcoyRJktST13zt7JG0e8z+z55w/huPOqehSJb2jTc8a8L5bzv6vIYiWdqXX/vMkbQrSZKaU/mZjhHxNOALFL0dnwPcADwd+DJwUkTsnJlLao1SkiRJ0sPGe4/73UjaPeIVzxhJu5IkLYumVSkcESsB3wV+AOxUTs7MXJSZ+wP3AwfUG6IkSZIkSZKkqaTq7dV3R8TGmZkAETENWL6lyHuBX0bEZ8bKSJIkSZIkSXp4qXx7dVsyMYD/aZk3PyK2MeEoSZIkaVlz0PHzRtLup/d56kjalSRpEJVur26XmYsz8+sR8dyWaTcOHpYkSZIkSZKkqapST8eICOBJwOVtvRmPBDauMzBJkiRJUncfPuHikbR76Eu2Gkm7kqSpoert1dOAY4HjImJr4E2ZeQtwd+2RSZIkSZKmrMNOvmwk7R68++YjaVeStLRKt1dn5mJgIfAF4DLgDxHxbGDxEGKTJEmSJEmSNAVVHkgGWJKZ9wIfiYhTKZKQkiRJkiRJkgT0l3RcJyIObfkcwGNapk0DZmXmfw0cnSRJkiRJkqQpp6+ejjz0GY7ZMm0asGiQoCRJkiRJkiRNXf0kHW/MzE8DRMTuwG+Bvcem9SIi1gZ+lJnbjTN/HeAC4Kpy0ksz8599xCpJkiRJkiSpYf0kHTMipgGfB3YHXk7R07EnEbEacDSw0gTFtgY+nplH9hGfJEmSJEmSpBGqNHp1mWycCewKrAlslpnnVWxzMbAvcMcEZbYB9ouIP0TEJyrWL0mSJEmSJGmE+unp+KHM/Blwcsu0iXotLiUz7wCIiImK/Rz4GHAPcEZEbJGZl3YqOH/+/F6bHopRtz8e46rGuKoxrmqMqxrjqsa4qjGuaoyrd5MxJjCuqoyrGuOSpIefOXPm9Fy2UtIxM5cAP2udFkX28Kgq9fTg3My8v6z/ImAO0DHpWOXLDsOo2x+PcVVjXNUYVzXGVc3EcZ3WWBztJo5rblNhPMTEcZ3bWBztJo7rD43F0W7iuK5oLI52E8d1dWNxtJswrtP/0VwgbSaM68wbmgukRddj6tk3NxNIm65xnXtLM4G06RrXhb9rJpA2XeO6aF4zgbTpGtdlFzcTSJuucV15WTOBtOkW12daYRB7AAAgAElEQVR+/seGIlnagS/cdCTtStKoVLq9GiAi/iciHt0yaQawdzlvRkT8Tw1xnR4Rj46IFYHnA5fXUKckSZIkSZKkBvRze/WjASLiB8BywO3AuhHxceA+YM+IOCEzz+6lsojYAdg0M7/UMvmjwFnAAuCrmfnnPuKUJEmSJGlK+PwvrxxJu+/aaeORtCtp2ddP0nExsATYEHgxsAqwFfArYGUggM9GxLsz85zxKsnM7ct/zwTObJt3FuCRT5IkSZIkSZqC+kk6jsnM/HtETAfuyMwzI2ImcDPwbYpekJIkSZIkSZIeZgZJOhIRLwQ+BCyKiLcDm1EkI79cR3CSJEmSJGl0jpw7mtHA37L95Bz0UFLvBkk6Tgf2B14J/BzYB9gD+EUNcUmSJEmSJHV01G//MpJ237DtBhPOP/b8axuKZGmv2ma9kbQrTaRS0jEiNqR4ZiPAoszcs5x+c2Y+u3yf9YYoSZIkSZIkaSqp2tPx/cBLgM8C90XEeUACcyLiaOCWmuOTJEmSJEmSNMVUSjpm5n4RcTLwPeDAzDwxImYA5wAfA9YETqk/TEmSJEmSJElTReVnOmbmSRFxEfDziLgR+ANwf2ZeBVxVd4CSJEmSJEnq3/Hz/j6Sdvd56rojaVeTQ18DyWTm3yLiuZl5cznp2TXGJEmSJEmSJGkKm9bvgi0JR0mSJEmSJEl6QN9JR0mSJEmSJEnqpK/bqyVJkiRJkqRBnHjJ9SNpd88tHzOSdh9u7OkoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmrl6NWSJEmSJElS6dQrbhpJu7tstvZI2h0WezpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSarVSJKOEbF2RPxmgvkzI+LkiDgnIl7fZGySJEmSJEmSBtN40jEiVgOOBlaaoNg7gHmZ+Sxg74hYpZHgJEmSJEmSJA1sxgjaXAzsC5w0QZntgfeV788Gngac1ang/Pnz64ytslG3Px7jqsa4qjGuaoyrGuOqxriqMa5qjKt3kzEmMK6qjKsa46rGuKrpHtf0RuJo1z2uWY3E0a57XMs3Eke77nFN1B9teLrHtWojcbSbrL/HVnPmzOm5bONJx8y8AyAiJiq2EnBd+f7fwNrjFazyZYdh1O2Px7iqMa5qjKsa46pm4rhOayyOdhPHNbepMB5i4rjObSyOdhPH9YfG4mg3cVxXNBZHu4njurqxONpNGNfp/2gukDYTxnXmDc0F0qLrMfXsm5sJpE3XuM69pZlA2nSN68LfNRNIm65xXTSvmUDadI3rsoubCaRN17iuvKyZQNp0jeuqPzYTSJuucV1zZTOBtOkW1xnXjSYJ0i2us2/6S0ORLK1bXBf869qGIllat7gumvf3hiJZWre4rrjk+oYiWVq3uOZfcVNDkSytW1y/+r/RnLeft9EafS03WQeSuQtYoXy/MpM3TkmSJEmSJEltJmsybx6wbfl+S+Ca0YUiSZIkSZIkqYpRPNNxKRGxA7BpZn6pZfLRwKkRsR2wKXDBSIKTJEmSJEmSVNnIejpm5vblv2e2JRzJzGuBnYBzgB0zc3HzEUqSJEmSJEnqx8h7Oo4nM68Hjh91HJIkSZIkSZKqmazPdJQkSZIkSZI0RZl0lCRJkiRJklQrk46SJEmSJEmSamXSUZIkSZIkSVKtTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSaqVSUdJkiRJkiRJtTLpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRamXSUJEmSJEmSVCuTjpIkSZIkSZJqZdJRkiRJkiRJUq1MOkqSJEmSJEmqlUlHSZIkSZIkSbUy6ShJkiRJkiSpViYdJUmSJEmSJNXKpKMkSZIkSZKkWo0k6RgRR0XEeRFx8DjzZ0TE3yJibvnavOkYJUmSJEmSJPWn8aRjRLwEmJ6ZzwQ2iIg5HYptAXwvM7cvX5c1G6UkSZIkSZKkfs0YQZvbA8eX738BbAvMbyuzDbBbRDwXuAzYPzMXdaps/vz2RZs16vbHY1zVGFc1xlWNcVVjXNUYVzXGVY1x9W4yxgTGVZVxVWNc1RhXNd3jmt5IHO26xzWrkTjadY9r+UbiaNc9rpUaiaNd97hWbSSOdt3jWq2RONq1xjVnTqe+g52NIum4EnBd+f7fwFM6lLkQ2DEzb4iIY4BdgJ92qqzKlx2GUbc/HuOqxriqMa5qjKuaieM6rbE42k0c19ymwniIieM6t7E42k0c1x8ai6PdxHFd0Vgc7SaO6+rG4mg3YVyn/6O5QNpMGNeZNzQXSIuux9Szb24mkDZd4zr3lmYCadM1rgt/10wgbbrGddG8ZgJp0zWuyy5uJpA2XeO6cjQ3rHWN66o/NhNIm65xXXNlM4G06RbXGdeNJinZLa6zb/pLQ5EsrVtcF/zr2oYiWVq3uC6a9/eGIllat7iuuOT6hiJZWre45l9xU0ORLK1bXH/7v0l63h7HKJKOdwErlO9XpvMt3pdm5v3l+98Dk/MvY0mSJEmSJEkPMYqBZOZR3FINsCVwTYcy34mILSNiOrAncElDsUmSJEmSJEka0CiSjicCr46II4B9gCsi4rC2MocC3wEuBs7LzDMajlGSJEmSJElSnxq/vToz74iI7YGdgMMz80baejJm5uUUI1hLkiRJkiRJmmJG8UxHMvNWHhzBWpIkSZIkSdIyZBS3V0uSJEmSJElahpl0lCRJkiRJklQrk46SJEmSJEmSamXSUZIkSZIkSVKtTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSaqVSUdJkiRJkiRJtTLpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRamXSUJEmSJEmSVCuTjpIkSZIkSZJqZdJRkiRJkiRJUq1MOkqSJEmSJEmqlUlHSZIkSZIkSbUy6ShJkiRJkiSpViYdJUmSJEmSJNXKpKMkSZIkSZKkWo0k6RgRR0XEeRFx8CBlJEmSJEmSJE0+kZnNNhjxEuBFmfm6iPgW8MnMnF+lzO23395s0JIkSZIkSZIeMHv27Jho/ih6Om4PHF++/wWwbZ9lJEmSJEmSJE1Co0g6rgRcV77/N7B2n2UkSZIkSZIkTUIzRtDmXcAK5fuV6Zz4nLBMt+6bkiRJkiRJkkZnFD0d5/Hg7dJbAtf0WUaSJEmSJEnSJDSKpOOJwKsj4ghgH+CKiDisS5lTGo4RgIh4aUQsX3fZOtXd7iD1RcSTI+L5dcXSVN2DGNV2r2IyxxgRz4mIcyJi7jiv30TEDiOO8eiIeEJE/GdE7Ndw233v93Vt917rGfLvf+QxDGIyxDXgsX3SHEMmw7rs1TBibWI79hN3E9uljjaqnnNGefzvEHsjv8O6zst1rrsmzgEPp2vYXtZnUzE3fX5paF8a6TlzmO2P+rtV0fTvbjJdq3ZrYzLFOsr2Joij7/NgP8sO+1pjMpwbG7+9OjPviIjtgZ2AwzPzRuCSLmVuj4jnAJ8AFo5T9XTgI5l55tiEiDga+BiwHTA9M7/Za5wRsRHwDuBHXcodDXwHOAyYDfTcRsMxdm2j1/paym8F7ArcAySwNTArIjYti0wDfpGZl/dS3zDqHmT99hBj1fVVeR8eVC8xDnMd9eC3wHMzc0FE7F1OWwO4MjPnRsQ0YNSPU1gILCr/zX4q6HXbUzzDto79vpZjw0T1DPIbrfJbmAwxtCzT67G00bi6qXqs6mfZYR1HhnmeqRhHL9vlJxSPhhlKrMPajv2s4yb2cWo6Hrapes4Z+Phfh0G2fR/qOi/Xsu6GeA7oZ78f+r477GvF8dbnKI61Ax7TKp9zhrUv9dpGr/FPpr8N+6m7pXwt11mT9XfXFutjgBcwomvVKt+9od/BpLxerrDfD3Ie7GfZns6XDf0WhnIuGMUzHcnMW3lwdOpeywxtA7aKiLnl23WBxcBZEQHFCr48M9/aVvaJFKNtzwIOiIhXdSo7gaHG2EsbfdQ35jrgVOB+YAnwdOA44Ipy/nTg+l6+0xDrrv0PhgHWV2MJtooxjuyPqsxcXMY33vwlDYazlIiYXsbXPn014CjggMz8S4/V9brtV2eA/b6uY0OP9QzyG+1lfZwZER8ecQz9nlOajqujAY5VtZ9nBjDM80wVvWyXNSn+8Kg11ga2Yz/ruIl9fKDjYSe9nnNqPv73bZBt369Bz8t1rbsGzkP9LNvEvjuUa8Vu65PiD9VGjrU17dc9n3Ma2JfqPmeO/G/DAeseU9d11mT93S0EjqXodPRI4LZx1s1kOV9+LSIWMcTfQdu6mYzXyz3FNch5sMqyfZwvm/gtDOW6eyRJx34MeQO22hHYHPg0sCewwVgmNyJmtbZRlv0hxYHm28Cjga+UbXx+ou/TVIw9ttFTfR1MBz7X8nkz4PHAgvLzoszccYLlJzJQ3UP+g6Gv9dVwgq1rjJPoj6oPAHtRJO4XA8sB90TE/cBZmfnBYcfQIabZwIkRsRjYhKInM8CLgFcAX6iybqr8octgv6m6jg1vpBjEa6J6+o61l/UREb18l6HGMPa+j99KI3H1oN9je8/LNnAcGeZ5pmc97rPBcGId9nbsZx0PfR+v4XjYUbdzDnA4NR7/BzTItu9bv+flms+dQz0H9LNsE/vuEK8Vu63P1fuNeQixjKvPc86w96Wev1Mv8Vf8brW33+f6e4i6r7OAjt+96d9dW6yvBF4GvBx4PnAM8KbM/EuT16q9fHeKTlJD/R1M1uvlfvb7Qf4+7WXZfs6XDV0/Dee6OzOnzAv4AMUgM5cBFwN/Kj+fC3y8LDOb4qLxDIpM7frAq4ATgF8Au3VpY1XgfGADYEPg1A5lxto4q9wA2wHvBW7ssY0mY+zaRi/1jRPHo4C55fv3A6sAawPvLaddMMC27rvuQddvD7H1tb563Ydr+q1MGOOw11HFWA+gOBG+B9gGeHP5eWPgkKbimCC+b7asn48D0Wc9vRy/BvpN1XVs6LGeQWOdcH1Mhhj6/a00EVeP+9wgx6pazzMD/P6Gdp6p+zc8rFgb2I6V427otzes9dnzOYeajv8D7HN9b/sB2x34vDzouhvWvjvosk3su720Ufe+NKzfW537NX2ec4a9L1VoY6L4zwQurfrdamx/oPU3yL7cS1w91jOKa8InAVcBpwG/AS4cwr5Vy3fvcR9p9O/vEW2zrnGVy/V9Hqy6LBXOl3XtDxPUP5RzwZTp6VhaAPw/4MnAecBWwJUUyb6XAWTm7cBzASKi9T79PwEHZ7m2JnAIsB5Fj8XlgU0j4jSKXqG/ycyPjrURxUA3W1Nkptei2ChLgHdHxFMz86OdGmgyxh7b6FrfOHHcV9a/LkU35RdRPIfzioh4LOM/b6AXfdddw/rt5hD6W1/Qwz5ck15iHOY66sdzgS9RjFj/RIpno0028wdYN71s+0F/U4dQw7GhPLZ1238GjbXb+uj6XRqIod/jydDj6tEh9H+s6rpsA8daGO55pqpu22VYsR7CELdjn3E3sY8Pe9tXPecMcvzv1yH0v+3rUNd5uZ91dwjDPQf0u2wT++4wrhUPYYL1SXmHVkPH2gljGcLfT720Nymuv/r4brW330/dE8RVy3VWRGzcrR5Gc034boo7HgP4O7DeKK5Ve2zjEIb4O5is18s1XKsOch7sZ9lu58thn4OGcu011ZKOY4axAcccBHwSuIPi+UxfzczdImJ2udO2l10LOJTi4bFvyMydxynbi2HE2Esbg9RHucw9wM7l57spbjWvQ5111/UHw6DrC4afYOs3xlH8UQWwLfDNzFwYEWdSPOB4jRHEsZSIeBSwTofpMzJzUZ/V9rLt+93v6zo2VKln0N/oeOtjMsQwkV5+K6OIq9Ug+0Pd55lBDfM8U1W37VJ3rE1tx37ibmIfH8a273rOGdLxv6o6rjf61fd5uaZ119Q5oN9lm9h367xWnHB9RsQjaop54Fj6qK/bOaeJfWlY58xR/W1YV91j6rrOmmy/u09R9DJbSNHbcdTXqhO10eR1davJer3cS1yD/H3a07IDnC+H/Vuo9VwwFZOOw96AUDyn8SsU3VSJiCcAJ0TEHpn515ZyawMvpMgy06XsQzQRY4U2ev3OnbwTeBxFd1wodsgndlmmV33XPeQ/GAZZX00l2LrGOEn+qALYG9gxIt7bMm1FigfZNi4idqIY3exKYLVy8mKKfRFgv4h4ZGZ+vGLVvW77QX5TAx8bqtQzYKzd1sdkiIGy7X5+K0OPqweDHKvqPs8MYpjnmSp62S7DiHXo27HPuJvYx4exPsc95wzx+N+vQbb9ICqfl4ew7po4B/SzbBP77jCuFcddn8CtNcRcSyxD+vupiX2pjuuvNfr4brW1X8P662Tg66wq9dD8NeE7KW6PP62YNbpr1R7aGPrvYLJeL/cZ1yB/n0647IDnyyZ+C7WeC6Zi0nFoGzCKh6j+EPhJZh4fEZsBSzLzqoh4J3BaRDyLojvrYRQr/07gFOClwHJl2TOA30XEJpl5S4d2moqxaxvAZ3qpr9P3oHg4Kpn5ujK2/crP3yw//7bDMr3qu+5h/sHQ6/ofZ32NGWqCrccYD6bomj0Z/qiaAbw7M+e2fY+Ngdc20H4nZwPPycz7I+JYiv3xbOAVUYzidzfwlj7q7bbtB9nv6zo27A+8Dfhal3qm9RtrD+vjdOBHPXyXYcYwyDmljmPjQMeJQY5VdZ9nBjyODPM804+JtkvtsTa0Hfv5HTWxjw9r23c75wzr+F9JTdcb/er3vFzLuhvyvjtmkP2riX23tmvFXtYnxfPph36sHfCYVvl83MS+VMc5s/xuX6MYAXlB67zxvlud7Q+6/gb5u6eX7Qo8j+Lv7pH+7tpiXZ0igfc7imcFzgYeC+xLkbBp7Fq1h+9+DkO+rp6s18t9xgWD/X3ay7KDnC+HeQ4azrVX9vEgyFG9gPcB23eYvjHwyfL9chTJPyiGsd+IIqt9MjCXIkH4uHHqXx7Yp3z/JOCSsc/ltE1a2lgV2KdsY1eKYcR/39LGDhN8j6Zi7NpGr/WNE8t6lA8aLT8/D9ipfP854IcDbOu+6x5k/fYQV9/rq9d9uIbfSdcYh7mO+oh3JjC9ibZG+epl2w+439d1bPg58JYe6hno999lfRze43cZZgx9n1OaiKuH/W2QY3ut55kBfzdDO8/0EcuE22UYsTa0HSvH3dBvbyjbnilyzhlk29fQ9kjX0TD33ZayfS3bxL7bSxt1r89h/d763bbjLNvP+biJfamOc+avKa6/Htfrd6u5/YHW3yD7co9xfbKHepq+JjwOeFcZ61zgLuAPY+uwjn2rQlzd2vhxj/vIIL+DSXm93E9cZdm+z4ODLNtD3cP+e3Io54IoK5gSImImRVZ+3KHCNTlEROSQdq5h1j1s7sMPX4Nu+6m03/cS67B/C5MhhnHanJRxTUVN/yYG2S5T6ffbqp+4m9jHp+r6VHMG2UcmWraJfXeynQMe7r+3h/v3H0Rd+/Ky+rubSufLEVxzTcptNmqj3B8GWtZjqCRJkiRJkqQ6TRt1AJIkSRJARKwSETMjwmtUSZKkKW4qDiQjSZKkKSwiNgK+mpk7tM3aH3gMcH9EnJ+ZJzUfnSRJkupg0lGSJEkDi4grKUZeXTBeEeC+zHwWsHCccv8EHgEcAzx9GHFKkiSpGSYdJUmSVIf7gD0z85r2GRGxNvCvzFxUTspy+jYUI0reTZGIfASwEvBCYJWI+L/MvLCB2CVJklQzn5cjSZKk2kTEhhFxWkRs0DL5OOCz5fz1gR2ANYBZmfmEzNwyM58GvAE4MTOfmpkbmXCUJEmaukw6SpIkqU5/AX4C/CYiXhwRbwZWBg4s569Ncev0asBWEfGtiDgnIi4AHg/MjohdIuIbowhekiRJ9fD2akmSJNUmMxP4WkScDfwUeCywRWYuKOdfEBE3AY/PzC9ExFzgtcDBwJ0UPSA3BP42ivglSZJUD3s6SpIkaRgWU/wH9+XA5yJipR6WuRFYF3gG8MshxiZJkqQhM+koSZKkWkXE1sDPgNcA21GMVH1KRCzXVm4OcAvwHYpbrhdR9HbcCvhdkzFLkiSpXt5eLUmSpNpExKeBvYC9MvOSctq+wPHAnIi4lmJ06u2BAzNz77LM04AbgD8D9wMrRMR9mbm4+W8hSZKkQUXx2B1JkiSpfxFxMbAncB9wX2beNk65ZwJvBj4BLKQYYOYZwMXAycAnKe7GORm4JzM/PPzoJUmSVDeTjpIkSRpYRPyJIom4YIJi04G3ZOb55TKrAHsAPwY2Bk6i6AX5GOAQ4GWZ+fchhi1JkqQhMekoSZKkRkTEDGBJZi4ZZ/7qmfnvhsOSJEnSEDiQjCRJ0jIoImZGxKxRx9EqMxeNl3As5/edcIyINSLiqxGxer91SJIkqT4mHSVJkiqIiHkRsUdE7BoRX4yI6R3KbBIROTYvIjaNiFkRsWFEbNXy2mycNlaPiJMiYr0BQt0cuC8int3l+xwcEUsiYlEPr4yIuQPENF4M/5+9Ow+Xq6oSNv6ujIQEAohigzYIBnFABhVBgkYUBaFBBsVWEFtxQGx7UD9sQUVF6aZtnIVWQGlsWkEZZRBajKACIi0qKhhBUFFQpkBkTFjfH/tcqFTq3lvDqeHC+3ueelK1z7Rqn6HOXdn77BkRMT8iNoyIrSJil4h4a0QcHRE/iIh3t7Gau4BXA3tNsq3NI+JN1fvrI2LniHhhQ9mZEfG3Eyy/ZUQ8GBHfm+C1PCI266AKGtf/nYg4sqls+6ruZzWV7x8RN3ezHUmSpH5z9GpJkqTOrAPMAS4B/h04NSL2zcwHI2JmNc/9wApgWkSsDnwT+DSwG7AAuIPy3MIrgF1bbGN/YBPgph7iXAoEcP0k890LXJSZL21npd20nqySr9Mz84Gm8kuB51Ge9bickjicTxnB+ufALcC5wB9arPMzlHpqNA/4bER8vKl8g8z8S/X+DuADEXEd5fmTDwL/D/hGtf92BI6a4OvcCyzNzIUTfN87aXq2ZURsRPkP/8aWnssy89amxe+gHD+N7gOyuf6qbUz0DE1JkqShMekoSZLUQpUsbNUr5D5K0nEp8CrgLZRE2a3V58Moyb7pwE+AtSkjM38KeBlwaGZ+JSK+BZzaYrvzgUOBdYFbI6Jx8prAFzLzbR18laWTTF/RtP1NM/NXTWXPB14B/HtmLmuadjKwZ/N6GkwHVgP+A2husbgzMAu4OzPvq9Z3A3BkZn5lkrhnAMdl5ritICNiLVZN4m1ESQDPr7a9BWWfLgVeRElCXtqwjmlNXcITmB8RP5ogtnnVfI3eATyNR+ppa+DkiHh/Zj7YMN9DY9sFpmXm8mqZVt3SV0k6Vq1CF2fmRPFJkiT1nUlHSZKk1q6lJP6ak2mrAccCn+GR5OLmwI6ZeTIlkbQR8Gvg7cDxwOsyMyPiAXg4ofkCoFU33i8ClwEbA/+UmRdWy7wDOBD4x+YFqkTlCuAv+cgogWOtLu9tmG8GsDowOzP/3GI9zwJ+HBGfBt7bkAw7Angi8HlgWdNixwHnUVoqthqhcBowG/hli2n3Avc0Jd2aY5oOzBxLSjZYPt4yLTTuw30picdDgMcDuwO3A2+itCydB9weJds7uyrbpGH52ZQk5nETbO9omu6zm5OjEfFd4E7ggYi4h5LshLJ//gZ4bxXjpybYzsP1HRFzKIndgyhJVZOOkiRpqEw6SpIktfYQsGdmnt9YGBFfAa7OzH+tWqORmQ9FxAbAdZSk3Fi33fOAu4FfRMR9lBaPAC8HLm8eOCUijqYkMLelJB3PioidKS0k30pJbDYn36AkNveu1tE87cEWZbdREqorycyrI2KPan3bRMSewCuB7YEtM/OWFstc1CKedn0BOKBFfCdFxEkNn1ew6n1rAm+OiH0mWH9QEoSzKa0ZAd4JvIeSeLyHkuCdBrwf+D3wzsw8NiK2p7SkfDo8kvys5nnDJN/r9cAtETEnM++tlt8beHZmfrBa15bVev6jMekaEV+nHF+Hr/JlHnlO5EwaWjhWx8inqu+xW2aeM0l8kiRJfWfSUZIkqbWHgNUiYl5D2V+A3wHrVZ93Bd4fEbtk5k2UVpBEGUH5B8DxmfnvYwtHxBnV232Bb7fY5reBT2bmUkqLw48BV1JaxG2ZmeMNGvIO4B9YuUXfrpTWeDtTunmPmU1pndlSZp4bEVsCpwM/rL7rIc1drmvytup1/1gLzap79WFj3aur1pmrjbP8FxtbEEbEi4B3ZOarWs1c7ctzgD8CCyn76GLgAEoL08cBT6pmfxIlwTjmecDJlCTmXTzSMrGVacCHgDkR8azMvIeqi31E3EbZH7dl5m8i4nUR8dvMvGSC9UHZZz+iJFJnVuv7GCU5fRxwJKXr/URxSZIkDYxJR0mSpNYeoiTeGq1BSTruVH3eH7gjM28bmyEi1gPOp7R6bB7QZMzJwBci4muZ+fBAL5l5TkTMqVoY7kdpDfc+SkvDyyPiLOA7lKTVb8cSTK2SkVXicDmwa2Z+q5Mvnpm3VC0Ir6MkKW/oZPkOttOq1WbzPMtZtUs3lLiaB1H5HbBPRGyWmdfAwy0UV6N0414WEa+nDEzzfsoAOucC50bECZTE3dho308BrmmI4zJg44jYDvgAcHk1aVq1zHd5pLvztpQWkz9qWP66iNgJ+B6lPoxdo9sAACAASURBVMcS0E8G/iEint/QNb6VFZnZmAAnIl5J6f791MxsHnxGkiRpqEw6SpIktTYHeElmXhQRzwD+r0paXQMcGhGbAnsA24wtEBEvAU6iDFAyE/hZ1XX4mZSEJQCZeVZEvJDSuu4lEfEESkvFbYFnUBJYXwLOy8wVwNER8SRgH0rX3R2A2yJi81bJpmoAlddRngH5mYj4XGZe2+4Xj4jVgK9SEmu/Bk6LiDdm5kkTL9n2+qdRnqd4H6s+MzOA2U0tTKEkDpc1JCrXAF4fEQdWn1enJIqXApc1dNkeaym5EaXl4jWUZOw8YGlE7F+9fz8lSfyHiHgipY5bfd/VgRdX8Y/Fu3W1jjGbU46flWTmLyPiLcDXgcOr4k8D76I8W/LMFtubzP0mHCVJ0igy6ShJktTampTu1FASSmMDr1wBPAH4MnBKZv4EICI2obSMvJ4yevDDA75ERLJqd9wPU0an3jgzr4+IWyldcse6y+5TLdsc11cpz2Nce4Jk0xHAzzPzxIjYHPhSRCxsGoW5papr+Dco94kvqxKtdwBfjoi7MrObxFizdSitDe9n1VGZVwc+x6oDqMwGXlPFBmVgm/dk5meruM8ArszMj0y04cycUz1f8Z8yc2FErAncCHwtMx+MiFOAoygjWf9di1XMpHR3/3jD5/+iDB4z9l3GBhlqZRfgt8DHIuLCzLwnIr4MbEV3SUdJkqSRNG3YAUiSJI2aquXhXGBs4JT5wK0AmXk3cCmwGfDw8wQz8zpgfUoy8o0RccPYq5qluSvwQ5Sk27xq+U9k5sWU1n9XZeaM5hclmfhgFrfTQtV9+A3Am6uiDwBrA5+LFhnMFsteU8X18sxcVsX2HuAE4BMRMWuidbQjM2/NzOmZuXpmzmt8URJyBzaXZ+bMzPxGw2qeSen+PWZD4MMRkU2vxnnGvBb4q4j4JGUwm7MburkfTWkl+vXM/FOLZa+l7Id1q9fjqvJ1G14fokWX9IjYldI69vmUAYY+Wk06pNXAMeOJiKdHxIfanV+SJGkYbOkoSZK0qu2BeykjEP81ZfToPwJExCJKl+plVK0XI+LfgKOrZyECnNCipWNULyLiKErX5wuBq5u23ZycbNbcHXlsG9OA91K67b5h7JmGVUu6PSmjNK8REW8eG1G5afnplK7dJwLvrbp1NzoY2DgzH6jmX5uSQLufR55lOJHplNaj/52Zd7Ux/7giYitKsu/71ecFwNOAdZuer/l2SrfllWTm3lUX6hOA5wLTI+KDlNan+1MGinlRRDwlM3/TtPj7KIPKLB/bTPXv6xvmmUVJPh7eEMuOwFeA/TLz5oh4G3BFRHy+SlhP+JWrdTwbOAR4FfA/wI9XmTFiB+AtwMG91rMkSVIvTDpKkiStag9KQjCAn1ESZm+PiNcBxwBvBP4WODEiDqC0eLyQ0jJyJvCmaiCWRjMpySiA44HjuhwReqXWilWycDdK67qnUpJapzTOk5nXRMTLKQPc/F9EvDMzL6wmT6/mWUFJWrZUJRuvaShan9JKcDmrdpFuZRrl3vNcSlJvPOOOrN3g7cAFmXlX9fzKr1KSvrc1zbc18MvGgoiYTRkI6B8o+2QrSoJ5Z8o+nAtsShlU5vsR8Q7g9IZBXg4D/jLWCrR6/uW9wLbVoDdExBwa9lNEPIcyavY7MvMcgMz8aTV4zHXVPI8D/hpYQBk1vNELqnq5kjII0eaZeW3VcnK9iNiI0uU7KF3Cd6T14DuSJEkDY9JRkiSpQUQ8j9K9do+qleACSiLqKMoAIq/OzPMj4hLK8x2XUBJvl1WrmA0c36Kl46zqxSSDuswCnlMt08pXmj5vRGmxdzWwZWb+utVCmXl5RDyXkrR6b0R8p0qSrUZp1XfnBDE1x7dVZv6cR5KoPYuIN1JalG4A3DHBfBtTuo/vXiXdjgZ+TtWqMCJmAKdRWlXuQEkgNzqWUmefyMyzqmXWAg6ltB48KjMfiIi3UgaXWZiZp1XzfZfyzMn7G3qqB2XwmsVNvddnR8TdmbljZl4ZETs0jmYNkJlXNXzcBjib0nrxtKaYz6YkSg9pOnYuo3RHb2yNeTfwlnae3ylJktRP8ch/2kqSJCki1gBelZknNJTNo7R8+1hm/rGhfENKK8dfZOYxVdlqlHusVbowt7n9ucCajdtpY5kNMvOmNuedBqyRmUu7iC0oicYH605qRcQelG7B3wY+OdH6I2K7zLw0Ip4PvAL4yFgrw2r6fpTk4E8z87Lx1jNKqrqdVz0zVJIkacoz6ShJkiRJkiSpVo5eLUmSJEmSJKlWJh0lSZIkSZIk1WpKDiSzdOlS+4RLkiRJkiRJQzJ//vyYaLotHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSarVjEFvMCIOAvatPq4FXF7F8QzgnMw8oprv+OYySZIkSZIkSaNv4C0dM/OYzFyUmYuAS4DrgOmZuR2wcUQsiIi9mssGHackSZIkSZKk7gy8peOYiNgAWA9I4JSq+AJgIbBVi7IlrdazZEnLYkmSJEmSJEk1WrCg/XaBQ0s6AgcDxwD7ATdVZbcDWwNzW5S11MmXlSRJkiRJktR/QxlIJiKmAS8GFgPLgDnVpHlVTK3KJEmSJEmSJE0Bw2rpuANweWZmRFxJ6T59GbAFcC3w+xZlE3rpzjv1L9oJ/O/5Fw5lu5IkSZIkSdKoGlbS8eXAxdX7M4BLImJ9YBdgW8pzHpvLJEmSJEmSJE0BQ+m2nJnvy8zTqvd3AYsorRpfnJlLW5UNI05JkiRJkiRJnRvmQDIPy8w7eGS06nHLJEmSJEmSJI0+B2iRJEmSJEmSVCuTjpIkSZIkSZJqZdJRkiRJkiRJUq1G4pmOj2Y77b7zULZ74VnnD2W7kiRJkiRJki0dJUmSJEmSJNXKpKMkSZIkSZKkWpl0lCRJkiRJklQrk46SJEmSJEmSamXSUZIkSZIkSVKtTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSaqVSUdJkiRJkiRJtTLpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRamXSUJEmSJEmSVCuTjpIkSZIkSZJqNbSkY0R8PiL+pnp/fERcGhGHNUxfpUySJEmSJEnS6BtK0jEidgCemJlnR8RewPTM3A7YOCIWtCobRpySJEmSJEmSOjdj0BuMiJnAF4FzI2IPYBFwSjX5AmAhsFWLsiWt1rdkScvigRn29sczqnFJkiRJkiRpalqwoP12gQNPOgKvB34BHAX8PXAwcHw17XZga2AucFNTWUudfNl+GPb2xzOqcUmSJEmSJOnRbxhJx62AL2TmzRHxFeAFwJxq2jxKl+9lLcokSZIkSZIkTQHDSOb9Gti4ev9cYCNK92mALYAbgCtblEmSJEmSJEmaAobR0vF44ISIeA0wk/JMx7MiYn1gF2BbIIFLmsokSZIkSZIkTQEDTzpm5t3AqxrLImIRsBNwVGYuHa9MkiRJkiRJ0ugbRkvHVWTmHTwyWvW4ZZIkSZIkSZJGnwO0SJIkSZIkSaqVSUdJkiRJkiRJtTLpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRamXSUJEmSJEmSVCuTjpIkSZIkSZJqZdJRkiRJkiRJUq1MOkqSJEmSJEmqlUlHSZIkSZIkSbUy6ShJkiRJkiSpViYdJUmSJEmSJNXKpKMkSZIkSZKkWpl0lCRJkiRJklQrk46SJEmSJEmSamXSUZIkSZIkSVKtTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrQaadIyIGRHx24hYXL02j4gPRcQVEfG5hvlWKZMkSZIkSZI0NQy6peOzgf/JzEWZuQiYBSwEtgH+FBEvjYjnNJcNOEZJkiRJkiRJPZgx4O1tC+wWES8GfgZcC3wjMzMivgXsAixtUfa/461wyZIlAwh7fMPe/nhGNS5JkiRJkiRNTQsWLGh73kEnHa8AXpqZf4yI/wLmUBKPALcD6wHLgeuaysbVyZfth2FvfzyjGpckSZIkSZIe/QaddPxpZt5fvf8RMJOSeASYR+nuvaxFmSRJkiRJkqQpYtAJvZMiYouImA68EphLeX4jwBbADcCVLcokSZIkSZIkTRGDbun4YeBkIICzgCOASyLiU8DO1etG4MimMkmSJEmSJElTxECTjpl5NWUE64dVo1PvCnwqM38zXpkkSZIkSZKkqWHQLR1XkZn3Al+frEySJEmSJEnS1OAgLZIkSZIkSZJqZdJRkiRJkiRJUq1MOkqSJEmSJEmqlUlHSZIkSZIkSbUy6ShJkiRJkiSpViYdJUmSJEmSJNXKpKMkSZIkSZKkWpl0lCRJkiRJklQrk46SJEmSJEmSamXSUZIkSZIkSVKtTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSaqVSUdJkiRJkiRJtTLpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRamXSUJEmSJEmSVKuhJR0jYr2I+HH1/viIuDQiDmuYvkqZJEmSJEmSpNE3zJaOHwfmRMRewPTM3A7YOCIWtCobYpySJEmSJEmSOjBjGBuNiB2BvwA3A4uAU6pJFwALga1alC1pta4lS1oWD8ywtz+eUY1LkiRJkiRJU9OCBe23Cxx40jEiZgHvB/YEzgDmAjdVk28Hth6nrKVOvmw/DHv74xnVuCRJkiRJkvToN4zu1e8FPp+Zd1aflwFzqvfzqphalUmSJEmSJEmaAnpK5kXEVuOUrzvBYi8FDo6IxcCWwN9Quk8DbAHcAFzZokySJEmSJEnSFNBr9+pPAi9qLIiINYFvNJePycwXNsy7GNgduCQi1gd2AbYFskWZJEmSJEmSpCmg46RjRJwPPAj8CLgjIv6TMvDLNErX6cOBO9pZV2Yuqta5CNgJOCozl45XJkmSJEmSJGn0ddPScS1KS8TlwAOUBOODlEFhZlfvs5MVZuYdPDJa9bhlkiRJkiRJkkZfN0nHeylJxXuBoCQZ7wdsjShJkiRJkiSp52c6AqwHbAAsorR63AC4u4b1SpIkSZIkSZqCehq9ukE0/StJkiRJkiTpMaqOlo63ADcBi6t/n44tHSVJkiRJkqTHrG5aOs6pXqtVn2cAM4E16wpKkiRJkiRJ0tTVTdLxbuA2YBZltOp1KUnI/SgDyszBbtaSJEmSJEnSY1bH3aszc6ex9xFxcWbu3jg9In4DfKmG2CRJkiRJkiRNQV0PJBMRc4H/aC7PzOuA1/USlCRJkiRJkqSpq6ukY0TMAq4Cfh8RazVN2x44robYJEmSJEmSJE1BXY1enZkPRMSawLuADSNiHnARcDbwBeCN9YUoSZIkSZIkaSrpqKVjRCyMiI2qjzdm5mszc3vgRcBWwIXA5zLz4lqjlCRJkiRJkjRldNq9ej/ggoi4CXhyRBwaEacB5wHnApsB+0XEJjXHKUmSJEmSJGmK6LR79Xsy8+6I2AE4CNi9WscLMvN+gIg4AjgGeFmtkUqSJEmSJEmaEjpNOr41IvYFLgZuAa4BfgucGxHXAlcCzwROrDVKSZIkSZIkSVNGp92rzwPeAfwJWBtYBzgdmFVN+zDw08z87zqDlCRJkiRJkjR1dNrS8dPAg5Rk43OASyhdqZ8EnA9cCxwYEedm5p/qDFSSJEmSJEnS1NBp0vEVwGHAn4EEbgM+AFwAXA78CPgacAjwrvrCVN1ets8rhrLdC75+7lC2K0mSJEmSpMHpNOl4IHAr8Efgh8D1wHbAncBCYP3M/HVEHFprlJIkSZIkSZKmjI6Sjpn5OYCImE3pTn0vpWv1zzPzHuDX1ay71hmkJEmSJEmSpKmj05aOAGTm/cD91ccbqlfj9Ht7ikqSJEmSJEnSlNXp6NWSJEmSJEmSNCGTjpIkSZIkSZJq1VX3aqlfXv7a3Yay3W+d/M2hbFeSJEmSJOnRaCgtHSNinYjYKSLWHcb2JUmSJEmSJPXPwJOOEbE28E1gG+A7EfH4iDg+Ii6NiMMa5lulTJIkSZIkSdLoG0ZLx2cD/5yZHwW+BewITM/M7YCNI2JBROzVXDaEOCVJkiRJkiR1YeDPdMzM7wJExAsprR3XAU6pJl8ALAS2alG2pNX6lixpWTwww97+eIyrM6MalyRJkiRJ0qhYsKD9doFDGUgmIgLYF7gDSOCmatLtwNbA3BZlLXXyZfth2Nsfj3F1ZlTjkiRJkiRJmoqGMpBMFgcDPwVeAMypJs2rYlrWokySJEmSJEnSFDCMgWQOiYjXVx/XAv6V0n0aYAvgBuDKFmWSJEmSJEmSpoBhdK/+AnBKRBwIXA2cAVwcEesDuwDbUrpcX9JUJkmSJEmSJGkKGMZAMncAOzWWRcSiquyozFw6XpkkSZIkSZKk0TeUgWSaVYnIUyYrkyRJkiRJkjT6HKBFkiRJkiRJUq1MOkqSJEmSJEmqlUlHSZIkSZIkSbUy6ShJkiRJkiSpViYdJUmSJEmSJNXKpKMkSZIkSZKkWpl0lCRJkiRJklQrk46SJEmSJEmSamXSUZIkSZIkSVKtTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSaqVSUdJkiRJkiRJtTLpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRazRh2ANJUsPMBewxlu+efeOZQtitJkiRJktQLWzpKkiRJkiRJqtXAk44RMT8izouICyLi9IiYFRHHR8SlEXFYw3yrlEmSJEmSJEkafcNo6fg64OjMfBlwM/AaYHpmbgdsHBELImKv5rIhxClJkiRJkiSpCwN/pmNmfr7h4+OB/YBPVp8vABYCWwGnNJUtabW+JUtaFg/MsLc/HuPqjHFJkiRJkiRNbMGC9tsFDm0gmYjYDlgbuAG4qSq+HdgamNuirKVOvmw/DHv74zGuzhiXJEmSJElSfYYykExErAN8BngjsAyYU02aV8XUqkySJEmSJEnSFDCMgWRmAacC/5KZNwJXUrpPA2xBafnYqkySJEmSJEnSFDCM7tVvonSXPjQiDgW+BOwfEesDuwDbAglc0lQmSZIkSZIkaQoYxkAyxwDHNJZFxFnATsBRmbm0KlvUXCZJkiRJkiRp9A1tIJlGmXkHj4xWPW6ZpJXt8uY9h7Ld8754+lC2K0mSJEmSpgYHaJEkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSarVSAwkI+nR4xVv33to2z73898Y2rYlSZIkSdIjTDpKekzY9R9fPbRtn/PJU4a2bUmSJEmShsHu1ZIkSZIkSZJqZdJRkiRJkiRJUq1MOkqSJEmSJEmqlUlHSZIkSZIkSbUy6ShJkiRJkiSpViYdJUmSJEmSJNXKpKMkSZIkSZKkWpl0lCRJkiRJklQrk46SJEmSJEmSamXSUZIkSZIkSVKtTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYzhh2AJD2W7fb/XjO0bX/zqK8ObduSJEmSpEc3WzpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVaihJx4hYLyIuqd7PjIizI+L7EfHG8cokSZIkSZIkTQ0DTzpGxNrAicDcqujvgSszc3tgn4hYY5wySZIkSZIkSVPAjCFscwWwL3Bm9XkR8N7q/cXAc8cp+06rlS1ZsqRPYbZn2Nsfj3F1xrg6Y1ydMS5JkiRJ0qPBggUL2p534EnHzLwLICLGiuYCN1XvbwfWG6espU6+bD8Me/vjMa7OGFdnjKszxiVJkiRJeqwZhYFklgFzqvfzKDG1KpMkSZIkSZI0BYxCMu9KYGH1fgvghnHKJEmSJEmSJE0Bw3imY7MTgXMjYgfgGcDllK7VzWWSJEmSJEmSpoChtXTMzEXVvzcCOwHfB16amStalQ0rTkmSJEmSJEmdGYWWjmTmH4BTJiuTJEmSJEmSNPpG4ZmOkiRJkiRJkh5FTDpKkiRJkiRJqpVJR0mSJEmSJEm1MukoSZIkSZIkqVYmHSVJkiRJkiTVyqSjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrUw6SpIkSZIkSaqVSUdJkiRJkiRJtTLpKEmSJEmSJKlWJh0lSZIkSZIk1cqkoyRJkiRJkqRamXSUJEmSJEmSVCuTjpIkSZIkSZJqNWPYAUiSRs/fHPbaoW377CNOHtq2JUmSJEn1sKWjJEmSJEmSpFqZdJQkSZIkSZJUK5OOkiRJkiRJkmpl0lGSJEmSJElSrRxIRpI0Zez+of2Htu2zPnjSuNP2OPKAAUaysjP/5cRxp73y4383wEhWdsa7vzS0bUuSJEkaPls6SpIkSZIkSaqVSUdJkiRJkiRJtRrp7tURcTzwDOCczDxi2PFIkqTe7fnpA4e27dPfedy40/Y65i0DjGRlpx30hXGn7X3C2wYYycq+8cZjx532qpMOHmAkKzt1/88NbduSJElqT2TmsGNoKSL2AnbPzDdExAnAkZm5BGDp0qWjGbQkSZIkSZL0GDB//vyYaPood69eBJxSvb8AWDi8UCRJkiRJkiS1a5STjnOBm6r3twPrDTEWSZIkSZIkSW0a5Wc6LgPmVO/n0ZAgnaz5piRJkiRJkqThGeWWjlfySJfqLYAbhheKJEmSJEmSpHaNctLxDGD/iDgaeDVwzpDj0QiLiFdFxGrDjmMUWTeqm8eUJGmY/B16bHF/j4Ze9oP7cHQMc18Ma9sef5PrZx2NbNIxM++iDCZzGfDizFzaj+1ExFYR8bJ+rLvDOCbdyaMSa7NhxxURmwJ/D9w/rBhaiYgTI+KpEfF3EXHgkGKYtG6GGWdEvCgivh8Ri8d5XRIRO45SDIOsr2GfW610er7VsY/b/REcxfqC3uKq6wZgEHXYzbKD+AOmy7j6fizVtY1BH/ejdD42bmNAx3jfbsh7PBfqOpZGZt9Osv2u7/v6/Rs+7LrpxCBj7aXe293f7W6jl/uSbpbt5ruP4nHU43lX6z5ssdyUuHaNZ5D3XoO4jx9vPw7r2l3H8dfLdaOb7zGoa03Dsn09R0f5mY5k5h08MoJ1LSJiS2BX4B4ggecDsyLiGdUs04ALMvPqOrc7SUxjO/nrw441Il4EfAx4cJxZpgOnU563OdQ6jIjF1dsnAyuA70TE2Pavzsy393P7bXgQWF79m4PccId1M7Q4ge9R/lPhgYjYpypbF7gmMxdHxDSg389w7TSGvtVXXed8RJwIfATYAZiemcfVENvi6m2n51tP+3i862M1rev6avNa98HMvKhhmfOBx1MGOgvgj62WoQx+Vsd+HPe7N8034f7uYx32dLy2+/06XbabuAZxLFHfcTHQ475p+aGej4xfhy8CXgM8OSJq27ftfvem+Tq+/nZ6LvTj/nDY+3a8Y65pXYurt73c97X1Gz6I87rL36FVjq9RjbVJx/dOXezvdrfRy31JN8tOGtcQ7mfavk71ct71cR82bqMv166m9bRVXwM6F7v6TgO+j19pPw7y2t2o5uOvrr9Z+3mdGnYdjWukk459chNwLiWL+xDwPOBk4OfV9OnAHwYRyGQ7mXJRGnSs7RzgjwfWH3BcrbwU2Bz4N+CVwMZjF9iImDWA7bcUEdMzc0WL8rWB44F3Z+b1fQ5j0roZhTir7a8SQ8P0h/q5/U5iGFB91XV96kditKvzrdt93OaPYC/11c2P+U2UG9vnA9sCi8dZZp0e4qrtBmAAddjVsgP4A6abuAZxLPV0XAw41pWM0PnYXIf/Avy52tYKSuJxr4h4qKa4+npD3sO5UNu97Ajt23Z0fd/XxW/4IM7ruv6oHNVYe713amt/d7qNXu49O1m2w7gGfY51cp/Yy99bfdmH1bTF1dt+Xbsa1Z0o6uVc7PY79f0+frz9COwDfK2K6+XtbnuidQ7pGtL136z9vE6NWB219FhMOk4HPtHw+ZnAU4AHqs/LM/OlA4plsp28zqBjbecAj3JFH4U6XB04Bngt8FfAUcArqjgfmGC5vomI+cAZEbECeDpwRDVp9yrOTw8g4QiT1M0IxUlEvA/YG5hFOfZmA/dExP3AdzLz0GHHQKm/QdRXT9enPidGuz7futzH7fwIdl1fXf6YJyv/7/Vc4K2UulgpQd1tXJW6bgDeDMybZD29xNrtsv3+A6abuPp+LNVwXAws1hZG4nxsUYfTquXmUf6zdmZmLqwrrko/b8i7PRfqvJcdiX3bpq5+h7q55xnEeV33H5VAy9/3QcfaEFuv95qT7u9ut9HLvWc7y3YR18gcRy3qq5e/t/q2D+nztataT18SRT3eD3S7bF/v4yfZj/sDG1Tb6+u1u9Pv3Mk2ur1u9PM6NWp1NK7MfEy9gCcCi6v3/wKsAawH/HNVdvkAY1mT8szKjYFNgHNHIVbgfZTRw38GXAX8svr8A+Cjo1KHwNGU7o3nU1oc/al6/7+UbgTDPtaOAzYC9qvqLQa47bbrZphxVtt/N+X5rf9EaT32turzZsDhoxZDP+url3MLmE9JkP4v5X9Bx2I8DbgA2G1Qx1Qd+5hJro+91lc1fcJr3Tj1ehTwG2Ap73ckmQAAIABJREFUcBfwuxbL9BpXO9990v3d7zrsdtl24uqxbjqOa0DHUi2/nYOIdYTPx+ZtrF/Nc0QV1519iKuW87HOc6GuY2mU9m2bsfZ830cHv+FdHpOD+B1a5fgalVjrqPdu93eH+7bre89Ol20nrlE5juo+7/q8D/t9j9NtffX1XOx22R73Y6/H/ECv3f06/jqth16/Rw31PtTrbOPrsdjS8T6AiHgypcXK7sBOwM8j4kmM/wyGfjgc2BD4PLAa8IwozwybAVwCfGpIsT4AvAvYCrgU2BK4BriZ0nVpVOrwEOBIyh/+jweOzczdImJ+9mngoR4syepMHZBu62bQcTZ6MfBZYAvgaZTnho56DHXXV9fnVrVfX1wt3/ismV8Ch9UQZx3nWyf1ezgTXB8z80P0fi2a7FrXql4fpHRj2QZ4f6tlaohr0u/ezv6OiKP7XIfdLjvp9+ulbrqMq+/HUg3bGGSszQ5nBM7HFts4jvK/7nOBlwCr9yGuSb97D9ffSdc9znJ13oe1E8MwjrlW6r7vm+w3fBDndTe/Q2Mar/ebjUKsbWr33qmX/d3uNnq59+xm2fHiGonjaJx197If+rkPD6eP164e6qvf141ulx30ffyYJcB/17DtldY55GtIXX+z9vM6New6WsnIjl49AI+nPIB1Z0oz5b9QmpQO0iHAs4E9KNniH2bmzsDeTTeaw4r1xZT/mUnKAf6UpumjUIenUuoPgIh4KnBJRDTHOlAR8URKM/Lm8kEm+ietmxGJE2AhcFxmPghcROkqsPWoxTDA+qrz3KozMdrL+dbpPm73+gi919dk17pW1m1jmW7j6uS7N2vc34Oqw06X7eX79fs7DeJYquv8HuRxP2rn49g2bge+Sfmj8whgWR/iqut8rHvdUM+xNGr7djJd/w718Bs+iPO6m7ppdXyNXKw93ju1tb+73EYv955tLdtFXKNyHDXr5f6vX/twkNeuRp0kivp5LnazbN/v48fbj5SBfgZ97Yb6j7+urxv9vE6NWB2t4rHY0nHMO4G/pjRRhnKSPm0IcZxKuVn+ATy8k0+LiD2AO6p5hhHrwwd4RFxEGTxh3aZ5hlaHUZ7VcSpwemaeEhHPBB7KzF9HxDuB8yNi+8y8dRDxNMS1E2V0s2uAtaviFZR6AjgwIh6XmR/tYwzt1M1hlP+FG1qcTfYBXhoR/9xQtjrlQcmDMm4MQ9ivXZ9bE/0gZObyboKp6XzrZh+Pe33MzN80zNfLtaida11jvd4CbAp8t1rmu8DbWy3TY1xtffc2bgAGUYfdLNtuXL0s201cfT+WetzGoGNtNDLnY9M2NqR0Nbqij3H1fD5OcP3t5Vyo61gapX3bUi+/Qz3+hg/ivO70d6i5fOx6PzKxVnF1Xe/t7m9Ka7Ju920v954TLtvDdx/qcdR8nerxvBvEPuz7tavL6/ogzsW2lx3EffwEx/xTKAnHm4EFlAFv+n7t7uPx1/F1o8ffoH5dawZ1jhbZRh/sR9MLeALVcxCqzwcCBzZ8/t6A4pgFnAn8Q/X5mcBZ1ftFwLXDipXy/ICrKP36G18/pDRlH3odUprRv7p6/yzgJ2Ofq7KnD+n4mg3Mrt5/hZKc2AA4u6rDc4C/HnbdjEKcDfG8F1jUonwz4MhRiGFQ9dXLuUXpWnEZ8OXq342AvwXeV01/G3Bov46pOvcx7V0f1+31WsQk17px6vWjwA2UB/YvBn5VvW9cppf92O53n2x/Hwz8op912M2y7X6/Huumm7gGcSzV8ts5iFhH+Hx8eBtVXD8G/qchrttqPsbrOh9Xuf62u+5+HAOjuG/biLXr3yG6/A1vJ+5B1E2bx9d5oxBrHfXeyf7uYd92fe/ZzrKdxjVCx1HzdaqX865v+5ABXLu6qa9BXDe6WbaX/VjDMX8OJdF4DmXwn75fu/t1/LVTD3V+jx7rfejX2ZW2NdHER+OL8j/ijSfqS4CdqvefAE4dUBztJIaGEutkB/io1KGvR8cLmAlMf6zHUMXR9blVxw/CqNRvBz+CPV2Luvwx/zjlfx1XqtemZXrZj3XdAJwHHNTPOuxm2Xa/36CPiwEdS7X8dg4i1hE+Hx/eRhXXhyl/GD4L+DNwac3HeD//aO7lXKjrWBqZfTuKr06PyT4e9+0cX0eOQqxT5UUP9329LDvBOkflOBrKfWIX9dX3a1e39dXv60avx0qX9T0SfycN+zXoeng01XtUX0hAREROkQrpZ6wRMZPStHZFF8tOmTqUphLPrc60U1/dXOt6uT62G9eo6CXWUf2e3cTVr2Op17i6XU+vsXZjVOtwVI/TXgz6O43qMVeHQRyTddXNVIpVnXHf9Ee/rpXDvB94NP6m6dHBpKMkSZIkSZKkWj2WR6+WJEnSCImINSJiZkR4jypJkjTFPZZHr5YkSdIQRMSmwLGZuWPTpLcC6wP3R8RlmXnm4KOTJElSHUw6SpIkqWcRcQ3wQPVqOQtwX2ZuDzw4znx/BtYC/gt4Xj/ilCRJ0mCYdJQkSVId7gNemZk3NE+IiPWA2zJzeVWUVfm2lJFB/0JJRK4FzAV2AdaIiF9l5hUDiF2SJEk183k5kiRJqk1EbBIR50fExg3FJwP/UU3fCNgRWBeYlZlPzcwtMvO5wJuAMzLzOZm5qQlHSZKkqcukoyRJkup0PXA6cElE7BkRbwPmAe+ppq9H6Tq9NrBlRJwQEd+PiMuBpwDzI+IVEfHFYQQvSZKketi9WpIkSbXJzAT+MyIuBs4CngQ8OzMfqKZfHhG3AE/JzE9HxGLgAOAw4G5KC8hNgN8OI35JkiTVw5aOkiRJ6ocVlP/gvhr4RETMbWOZm4EnA9sAF/YxNkmSJPWZSUdJkiTVKiKeD3wTeD2wA2Wk6nMiYnbTfAuAW4GTKF2ul1NaO24J/HCQMUuSJKledq+WJElSbSLi34C9gb0z8ydV2b7AKcCCiLiRMjr1IuA9mblPNc9zgT8C1wL3A3Mi4r7MXDH4byFJkqReRXnsjiRJktS9iLgKeCVwH3BfZt45znzbAW8DPgY8SBlgZhvgKuBs4EhKb5yzgXsy8wP9j16SJEl1M+koSZKknkXELylJxAcmmG06cFBmXlYtswawB/ANYDPgTEoryPWBw4HXZObv+hi2JEmS+sSkoyRJkgYiImYAD2XmQ+NMXyczbx9wWJIkSeoDB5KRJEl6FIqImRExa9hxNMrM5eMlHKvpXSccI2LdiDg2ItYZ5LKDFBHPjoiDImJmQ9nWEfGSYcYlSZLUiklHSZKkDkTElRGxR0TsGhGfiYjpLeZ5ekTk2LSIeEZEzIqITSJiy4bXM8fZxjoRcWZEbNhDqJsD90XECyf5PodFxEMRsbyNV0bE4h5iGi+GGRExPyI2jIitImKXiHhrRBwdET+IiHe3sZq7gFcDe3URQtvLRsQHI2JZRNwwwev3EfHncZZfNyIOblF+TERsM8nmXwv8XWY+2FD2buDkVsehJEnSMJl0lCRJ6sw6wBzKwCcvAU4da3lWtS6cSRl9eQUwrXpu4TeBtwP/SXlu4ZeBC4CjxtnG/sAmwE09xLkUCOD6Sea7F7goM2e08QrgZZ0GEhHTW7W6jIhLI2I55VmQtwL/B1wBfJxStzOBc4E/tFj2MxFx59gL+BOwJvDZxvLqNbeuZYHlwNczc6PxXsBCyoA6rawOfCQiDmqIZwHwlgmWoTqu9q/qZqzs6ZTBe35FGZBHkiRpZJh0lCRJaiEiVo+Iec0vSmJoDiWp9yrgd8D8arFXAT8BzqEMmvITSkLoWuBTlEFWDs3MLYEfA6e22O584FDgGcCtTQmwhyLi2A6/ytJJpq9o2v6mLWJ6fkR8KCLmZeYDTdNOjoh7q9Z/rV73UhJ1H2ux7Z2BvwLmZObMzHwc8HvgyMx8dWb+fWYekZknt1h2BnBcZq7V8JqRmauNfQY2ouyb+2tcdnmrSmwhWhVm5m+BA4DdGooPAE7NzJ9OsL59gdnAaQARMQ04hjLgziuBgyLi9W3GJkmS1Hczhh2AJEnSiLoWWJempBywGnAs8BlKYmk6pSvzjlVy7OSI2Aj4NaV14/HA6zIzI+IBKAlN4AXA37bY7heBy4CNgX/KzAurZd4BHAj8Y/MCVaJyBfCXfGSUwLHn/t3bMN8MSku72Zm5SvffiHgW8OOI+DTw3oZuvEcATwQ+DyxrWuw44DxKMq7VCIXTKMmyX7aYdi9wT1N34eaYpgMzM7O5FWC7yT9YdR/2suwKYI+IuHqS5X7TXBARC4HvULVgjYgbmqb/EZiRmY9vKl8N+AhwZ2YurxKOx1KOv49n5kMRsRuwuNqH78/M5mSpJEnSQJl0lCRJau0hYM/MPL+xMCK+Alydmf9aJX+okj4bANdRknIPULoMnwfcDfwiIu6jdMkGeDlwefPAKRFxNCWBuS0l6XhWROxM6dL8Vkpis1UX3OOBvat1NE97sEXZbZSE6koy8+qI2KNa3zYRsSelFd32wJaZeUuLZS5qEU+7vgAc0CK+kyLipIbPK1j1vjWBN0fEPhOsPygtFWezctflXpZ9CDgDOAi4vyHJu/LCVZfyppah9wI3ZuZTx1lmIfDVFpMOp3T/viMi1ga+BDwe2G1sYJ7M/FmU53eeA7wmInbIzBsn+H6SJEl9ZfdqSZKk1h4CVmvqXh2U7tTrVfPsClwWEY/LzJuq7rnrAs8CbgQ+kJlPyMwnVs/6G7Mv8O0W2/w28PLMXJqZP6Z0Sb4SOARYlJmrPNuw8g7gSZSuymOvA6tpOzeVbwSMO2BJZp4LbElpwflDSrfwQzLzV+Mt04O3UbqqT8vMqJ4ZeSOwf8PnmcBa4yz/xaZnKR4AXNFQtmG1T1olartddg6wPqXr/FURcVXVjfyW6v1VEXFVNb15wJh27r1XSmJGxPMpg8UcVhVtTElq7wd8q2rlSkTsDhwNPJty3JlwlCRJQ2VLR0mSpNYeAk5vKluDknTcqfq8P3BHZt42NkNErAecT2n1+HFaOxn4QkR8LTMfHuglM8+JiDlVC8P9KMm/91FaGl4eEWdRuuf+BPjtWLfkzLy5eQMRsSWlG/GumfmtTr54Zt5StQK8jtLS74ZOlu9gO+MOnNIwz3JW7dINJa4Hmsp+B+wTEZtl5jXwcPfs1SjduLOGZecCv8jMl0fEmsCHKcnZdwF/oRwzZ2Xm8ePEvElEtGwdWbm16ftfHhEvoXo2Z2ZeCby6alm7BWXkbSjJ2Sdk5l2UgYokSZKGypaOkiRJrc0BXlK1tnsmpSvtMuAaStfjTYE9gP83tkCVHPox8GRgQ+BnEXF1RGQ1CA0AmXkW8BXK8xuJiCdExEcj4tuU0ab3pXSh3TQzj87MvSmJx+uA11OSXD+PiNmtAo+ItYDXUVo7viEintbJF6+eIfhV4HLgBOC0iNi/k3VMsv5pEbFeRMxvMVBPALNbDOKzbhXXmDWAgxsG2XkA+AUlOXdZPDIy9dLqtUFNyz6JMsDP04BbgDcBzwEupnSffyplFOwvNu+fzPzeWAvO6rh6HLBuY1nz8xyr5b7bohrnArc0JENXsOrzJyVJkobGlo6SJEmtrUlpuQYwDxgbeOUK4AmU1mSnZOZPACJiE0ort+uBxZn58IAvVcu25sFSPkxJXm2cmddHxK3AhyjdZ48D9qmWbY7rq5TnMa49wWAhRwA/z8wTI2Jz4EsRsXDs+X8TiYh1gG9Q7hNflpnLIuIO4MsRcVdmnjnZOtqwDvAHyjMTm2NaHfgcpVt3o9nAa6rYoAxs857M/GwV9xnAlZn5kTa238uym1Basv6K8mzOqymtDW8GnpuZN1StXY+ldFGn2kZQBhb6IvAG4CJKwvJ5EfGy6rmgcyj77iOZeeckcawD/KmNeCVJkobClo6SJElNIuIJVC3JqqL5VN1eM/Nu4FJgM8qz9qjKr6M86+/LwBsj4oaxVzVLc3fehyhJt3nV8p/IzIspg5ZclZkzml+UhNT/b+/e4+Qs6/v/vz+7m0AEGgc5ieABjQVUTlKBwAbWxqpfracd1BKLCql4qNR4+KrUb6qp1Wp/X6PYiqjgoZ4Ks9ZWsdZABnYlCVZ46LcqW+ODYANyioxLxUAyu5/fH/fsZmYyOzuzcx+umXk9H499ZO97Dtdn7vuae2Y/+VzXtdcjD6oBM7tQUVLrzyq71kvKSfoHa5DBbPDYyUpcz69Udsrd36Wo4nGjmS1t9hytcPdd7j7o7o9x94OrfyT9t6S19fvdfYm7j1U9zTMUVX7OepKkDZWq0uqf6vt09NhKpeUpiqpZD5C01d0frAwBr3a/opXJd1ftO03R+ZuR9BeKkpd/ragq9t2V++yV9HRJ362ujJ3H6YpWSAcAAAgSlY4AAAD7O1tRwug+M3uiotWj75EkMztP0UIsv1WletHMPiLpY5W5ECXp6gaVjlb5kZl9VNHQ502KKuWq1Scn6zUcQmvRStrvUbTS8etm5yV0999V5ojcJukQM/szd9/d4PGDkk6U9EVJ73H3+nbeIum42dWYK6sov1RRgrLZHIWzBhVVj36lMu/gopnZqYqGJt9c2V4h6fcVDVWunl/zzZJeEtdjJY1I+o2ixOwnJY2a2Wy1aU7S982srMoQcUWJxtnFf14s6TvuXq485tHKublAUqlyn6dp3yJD10j6X9WhV8X2GEmXKqqWBQAACBJJRwAAgP29VFFC0CT9p6KE2ZvNbI2kKyRdpKiS7Ytm9lpFFY+bFFVGLpF0cWUhlmpLJM1WCV4l6XOLXBG6plqxkix8saKh2U+T9Bp3v6b6Pu4+aWbPVzQs+DYzu9TdN1VuHqzcZ1pR0rKhSrJxsmrX0ZI+o2ixmgWHbSsaYTMk6Tvat/hJI4NNbpv1Zknfc/eHKvNXfl1R0vfXdfc7TdLtMT72nZKurbyOv3D3P5+9oTI8/hx3v7Nq3wGVZLApGkp9aeWmvaqsyF1ZGGY2kXhjpY2Xad9iRbMOlDRQGYL9NUXH8J+qbh9Ua8cOAAAgFSQdAQAAqpjZHyhahOW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      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对二手房区域分组对比二手房数量和每平米房价\n",
    "df_house_count = df.groupby('Region')['Price'].count().sort_values(ascending=False).to_frame().reset_index()\n",
    "df_house_mean = df.groupby('Region')['PerPrice'].mean().sort_values(ascending=False).to_frame().reset_index()\n",
    "\n",
    "f, [ax1,ax2,ax3] = plt.subplots(3,1,figsize=(20,15))\n",
    "sns.barplot(x='Region', y='PerPrice', palette=\"Blues_d\", data=df_house_mean, ax=ax1)\n",
    "ax1.set_title('成都各大区二手房每平米单价对比',fontsize=15)\n",
    "ax1.set_xlabel('区域')\n",
    "ax1.set_ylabel('每平米单价')\n",
    "\n",
    "sns.barplot(x='Region', y='Price', palette=\"Greens_d\", data=df_house_count, ax=ax2)\n",
    "ax2.set_title('成都各大区二手房数量对比',fontsize=15)\n",
    "ax2.set_xlabel('区域')\n",
    "ax2.set_ylabel('数量')\n",
    "\n",
    "sns.boxplot(x='Region', y='Price', data=df, ax=ax3)\n",
    "ax3.set_title('成都各大区二手房房屋总价',fontsize=15)\n",
    "ax3.set_xlabel('区域')\n",
    "ax3.set_ylabel('房屋总价')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, [ax1,ax2] = plt.subplots(1, 2, figsize=(15, 5))\n",
    "# 面积的分布情况\n",
    "sns.distplot(df['Size'], bins=20, ax=ax1, color='r')\n",
    "sns.kdeplot(df['Size'], shade=True, ax=ax1)\n",
    "# 面积和出售价格的关系\n",
    "sns.regplot(x='Size', y='Price', data=df, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>District</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Year</th>\n",
       "      <th>Size</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Renovation</th>\n",
       "      <th>PerPrice</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>东</td>\n",
       "      <td>新都城区</td>\n",
       "      <td>6室5厅</td>\n",
       "      <td>低楼层 (共6层)</td>\n",
       "      <td>2010</td>\n",
       "      <td>415.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70年</td>\n",
       "      <td>2.117013</td>\n",
       "      <td>880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>南</td>\n",
       "      <td>武侯立交</td>\n",
       "      <td>5室3厅</td>\n",
       "      <td>低楼层 (共3层)</td>\n",
       "      <td>2013</td>\n",
       "      <td>452.05</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.570623</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region District Layout      Floor  Year    Size Elevator Renovation  \\\n",
       "1098      东     新都城区   6室5厅  低楼层 (共6层)  2010  415.68      NaN        70年   \n",
       "1099      南     武侯立交   5室3厅  低楼层 (共3层)  2013  452.05        无         毛坯   \n",
       "\n",
       "      PerPrice  Price  \n",
       "1098  2.117013  880.0  \n",
       "1099  1.570623  710.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Size']>400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax1= plt.subplots(figsize=(20,20))\n",
    "sns.countplot(y='Layout', data=df, ax=ax1)\n",
    "ax1.set_title('房屋户型',fontsize=15)\n",
    "ax1.set_xlabel('数量')\n",
    "ax1.set_ylabel('户型')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "其他     1919\n",
       "精装      457\n",
       "简装      399\n",
       "毛坯      216\n",
       "70年       7\n",
       "Name: Renovation, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Renovation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Renovation'] = df.loc[(df['Renovation'] != '70年'), 'Renovation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画幅设置\n",
    "f, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(20, 8))\n",
    "sns.countplot(df['Renovation'], ax=ax1)\n",
    "sns.barplot(x='Renovation', y='Price', data=df, ax=ax2)\n",
    "sns.boxplot(x='Renovation', y='Price', data=df, ax=ax3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "有       2325\n",
       "无        469\n",
       "暂无数据     197\n",
       "Name: Elevator, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    df['Elevator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Floor'] = df['Floor'].str.extract('.*?共(\\d+).*?', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df['Floor']>str(6))&(df['Elevator']=='暂无数据'), 'Elevator'] = '有'\n",
    "df.loc[(df['Floor']<=str(6))&(df['Elevator']=='暂无数据'), 'Elevator'] = '无'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "有    2361\n",
       "无     630\n",
       "Name: Elevator, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    df['Elevator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_mean = df.groupby('Elevator')['Price'].mean().sort_values(ascending=False).to_frame().reset_index()\n",
    "\n",
    "f, [ax1,ax2] = plt.subplots(1, 2, figsize=(20, 10))\n",
    "sns.countplot(df['Elevator'], ax=ax1)\n",
    "ax1.set_title('有无电梯数量对比',fontsize=15)\n",
    "ax1.set_xlabel('是否有电梯')\n",
    "ax1.set_ylabel('数量')\n",
    "sns.barplot(x='Elevator', y='Price', data=df_mean, ax=ax2)\n",
    "ax2.set_title('有无电梯房价对比',fontsize=15)\n",
    "ax2.set_xlabel('是否有电梯')\n",
    "ax2.set_ylabel('总价')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~df['Year'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program\\python\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['Year'] = df.Year.apply(lambda x: int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x4bf8c0cb00>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 872x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df, row='Elevator', col='Renovation', palette='seismic')\n",
    "grid.map(plt.scatter, 'Year', 'Price')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax1= plt.subplots(figsize=(20,5))\n",
    "sns.countplot(x='Floor', data=df, ax=ax1)\n",
    "ax1.set_title('房屋户型',fontsize=15)\n",
    "ax1.set_xlabel('数量')\n",
    "ax1.set_ylabel('户型')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program\\python\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['Floor'] = df.Floor.apply(lambda x: int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>District</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Year</th>\n",
       "      <th>Size</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Renovation</th>\n",
       "      <th>PerPrice</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>东</td>\n",
       "      <td>南湖</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>34</td>\n",
       "      <td>2010</td>\n",
       "      <td>76.48</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.830544</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>西南</td>\n",
       "      <td>水碾河</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>6</td>\n",
       "      <td>1995</td>\n",
       "      <td>65.49</td>\n",
       "      <td>无</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.099404</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东南</td>\n",
       "      <td>成外</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>18</td>\n",
       "      <td>2011</td>\n",
       "      <td>114.48</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.135570</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>南</td>\n",
       "      <td>万象城</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>33</td>\n",
       "      <td>2012</td>\n",
       "      <td>80.54</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>2.222498</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>东南</td>\n",
       "      <td>成外</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>121.92</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.468176</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>西南</td>\n",
       "      <td>衣冠庙</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>15</td>\n",
       "      <td>2005</td>\n",
       "      <td>67.28</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.739001</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>东南</td>\n",
       "      <td>营门口</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>6</td>\n",
       "      <td>2000</td>\n",
       "      <td>189.38</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.240891</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>东南</td>\n",
       "      <td>红牌楼</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>122.79</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.759101</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>南</td>\n",
       "      <td>光华大道沿线</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>34</td>\n",
       "      <td>2014</td>\n",
       "      <td>84.77</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.356612</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>南 北</td>\n",
       "      <td>金融城</td>\n",
       "      <td>5室2厅</td>\n",
       "      <td>30</td>\n",
       "      <td>2013</td>\n",
       "      <td>254.22</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>4.326961</td>\n",
       "      <td>1100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>东南</td>\n",
       "      <td>龙泉驿城区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>21</td>\n",
       "      <td>2005</td>\n",
       "      <td>62.91</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.112701</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>南</td>\n",
       "      <td>蓝谷地</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>119.02</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.638380</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>南</td>\n",
       "      <td>大源</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>31</td>\n",
       "      <td>2014</td>\n",
       "      <td>76.49</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.614721</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>东</td>\n",
       "      <td>建设路</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>2002</td>\n",
       "      <td>122.49</td>\n",
       "      <td>无</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.894032</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>北</td>\n",
       "      <td>广都</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>18</td>\n",
       "      <td>2007</td>\n",
       "      <td>125.82</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.510094</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>南 北</td>\n",
       "      <td>橡树湾</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>33</td>\n",
       "      <td>2013</td>\n",
       "      <td>48.13</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.246624</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>东</td>\n",
       "      <td>驷马桥</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>50.08</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.797125</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>东北</td>\n",
       "      <td>一品天下</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>28</td>\n",
       "      <td>2012</td>\n",
       "      <td>83.60</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.870813</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>南</td>\n",
       "      <td>紫荆</td>\n",
       "      <td>4室3厅</td>\n",
       "      <td>6</td>\n",
       "      <td>2002</td>\n",
       "      <td>137.64</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.474862</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>南 北</td>\n",
       "      <td>温江老城</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>129.42</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>0.904033</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>西南</td>\n",
       "      <td>三圣乡</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>34</td>\n",
       "      <td>2009</td>\n",
       "      <td>86.85</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.807714</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>西北</td>\n",
       "      <td>猛追湾</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>22</td>\n",
       "      <td>2000</td>\n",
       "      <td>186.17</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.987431</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>北</td>\n",
       "      <td>中和</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>26</td>\n",
       "      <td>2013</td>\n",
       "      <td>143.03</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.943648</td>\n",
       "      <td>278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>东南 西北</td>\n",
       "      <td>沙湾</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>2000</td>\n",
       "      <td>92.30</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.300108</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>南</td>\n",
       "      <td>华阳</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>130.00</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.538462</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>东</td>\n",
       "      <td>万象城</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>18</td>\n",
       "      <td>2008</td>\n",
       "      <td>116.20</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.936317</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>东</td>\n",
       "      <td>大源</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>31</td>\n",
       "      <td>2014</td>\n",
       "      <td>89.05</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>2.796182</td>\n",
       "      <td>249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>东北</td>\n",
       "      <td>成外</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>25</td>\n",
       "      <td>2012</td>\n",
       "      <td>115.73</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.728160</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>东北</td>\n",
       "      <td>橡树湾</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>25</td>\n",
       "      <td>2016</td>\n",
       "      <td>93.86</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>0.990837</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>北</td>\n",
       "      <td>沙河堡</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>34</td>\n",
       "      <td>2011</td>\n",
       "      <td>81.47</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.062109</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>西南</td>\n",
       "      <td>猛追湾</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>39</td>\n",
       "      <td>2011</td>\n",
       "      <td>76.73</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.020070</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>西南</td>\n",
       "      <td>茶店子</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>30</td>\n",
       "      <td>2009</td>\n",
       "      <td>75.85</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.043507</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2969</th>\n",
       "      <td>东南</td>\n",
       "      <td>武侯立交</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>11</td>\n",
       "      <td>2004</td>\n",
       "      <td>78.53</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.171527</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2970</th>\n",
       "      <td>东</td>\n",
       "      <td>新双楠</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>113.02</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>2.265086</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>西</td>\n",
       "      <td>南湖</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>26</td>\n",
       "      <td>2009</td>\n",
       "      <td>124.54</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.365023</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2972</th>\n",
       "      <td>西北</td>\n",
       "      <td>外双楠</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>273.91</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>2.555584</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2974</th>\n",
       "      <td>南</td>\n",
       "      <td>东光小区</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "      <td>82.30</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.336574</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>西南</td>\n",
       "      <td>东苑</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>26</td>\n",
       "      <td>2009</td>\n",
       "      <td>135.00</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.837037</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>西</td>\n",
       "      <td>外光华</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>21</td>\n",
       "      <td>2013</td>\n",
       "      <td>80.11</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.059668</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>西北</td>\n",
       "      <td>人民公园</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>32</td>\n",
       "      <td>2009</td>\n",
       "      <td>88.21</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>2.324000</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2978</th>\n",
       "      <td>东</td>\n",
       "      <td>驷马桥</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>21</td>\n",
       "      <td>2010</td>\n",
       "      <td>68.37</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.608893</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979</th>\n",
       "      <td>西南</td>\n",
       "      <td>南湖</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>34</td>\n",
       "      <td>2010</td>\n",
       "      <td>178.76</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.510405</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2980</th>\n",
       "      <td>东南</td>\n",
       "      <td>国宾</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>30</td>\n",
       "      <td>2013</td>\n",
       "      <td>86.97</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.655743</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2981</th>\n",
       "      <td>西南</td>\n",
       "      <td>国宾</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>26</td>\n",
       "      <td>2011</td>\n",
       "      <td>92.41</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.698950</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>南 北</td>\n",
       "      <td>南湖</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>6</td>\n",
       "      <td>2002</td>\n",
       "      <td>90.43</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.017361</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>东南</td>\n",
       "      <td>高升桥</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>7</td>\n",
       "      <td>2000</td>\n",
       "      <td>56.09</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.230166</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>东南</td>\n",
       "      <td>茶店子</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>1999</td>\n",
       "      <td>131.58</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.367989</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2985</th>\n",
       "      <td>东南</td>\n",
       "      <td>蜀汉路</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>12</td>\n",
       "      <td>2006</td>\n",
       "      <td>63.98</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.594248</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>东 东北</td>\n",
       "      <td>驷马桥</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>18</td>\n",
       "      <td>2010</td>\n",
       "      <td>116.76</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.301816</td>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>南</td>\n",
       "      <td>草金立交</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>11</td>\n",
       "      <td>2005</td>\n",
       "      <td>147.21</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.358603</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>西</td>\n",
       "      <td>大源</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>31</td>\n",
       "      <td>2012</td>\n",
       "      <td>140.88</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.519875</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989</th>\n",
       "      <td>东</td>\n",
       "      <td>大源</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>29</td>\n",
       "      <td>2014</td>\n",
       "      <td>108.00</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.083333</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>南</td>\n",
       "      <td>外双楠</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>9</td>\n",
       "      <td>2003</td>\n",
       "      <td>76.00</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.684211</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2991</th>\n",
       "      <td>东</td>\n",
       "      <td>成渝立交</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>33</td>\n",
       "      <td>2008</td>\n",
       "      <td>97.36</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.869351</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>东南</td>\n",
       "      <td>华阳</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "      <td>157.13</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.463756</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>东南</td>\n",
       "      <td>成外</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>18</td>\n",
       "      <td>2011</td>\n",
       "      <td>85.23</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.290625</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>东北</td>\n",
       "      <td>万象城</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>28</td>\n",
       "      <td>2009</td>\n",
       "      <td>69.29</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.804012</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>东南</td>\n",
       "      <td>神仙树</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>7</td>\n",
       "      <td>1995</td>\n",
       "      <td>83.84</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.097328</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>东南</td>\n",
       "      <td>万象城</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>32</td>\n",
       "      <td>2009</td>\n",
       "      <td>89.84</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>2.215049</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>东北</td>\n",
       "      <td>东郊记忆</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>33</td>\n",
       "      <td>2009</td>\n",
       "      <td>90.04</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.754776</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2784 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region District Layout  Floor  Year    Size Elevator Renovation  \\\n",
       "1         东       南湖   2室1厅     34  2010   76.48        有         其他   \n",
       "2        西南      水碾河   2室1厅      6  1995   65.49        无         简装   \n",
       "3        东南       成外   3室1厅     18  2011  114.48        有         其他   \n",
       "4         南      万象城   2室1厅     33  2012   80.54        有         简装   \n",
       "5        东南       成外   3室2厅     16  2011  121.92        有         精装   \n",
       "6        西南      衣冠庙   2室1厅     15  2005   67.28        有         简装   \n",
       "7        东南      营门口   4室2厅      6  2000  189.38        无         其他   \n",
       "8        东南      红牌楼   3室2厅      7  2003  122.79        无         其他   \n",
       "9         南   光华大道沿线   3室1厅     34  2014   84.77        有         毛坯   \n",
       "10      南 北      金融城   5室2厅     30  2013  254.22        有         其他   \n",
       "11       东南    龙泉驿城区   2室1厅     21  2005   62.91        有         其他   \n",
       "12        南      蓝谷地   3室2厅     11  2006  119.02        有         其他   \n",
       "13        南       大源   2室2厅     31  2014   76.49        有         其他   \n",
       "14        东      建设路   3室2厅      7  2002  122.49        无         简装   \n",
       "15        北       广都   3室2厅     18  2007  125.82        有         其他   \n",
       "16      南 北      橡树湾   2室2厅     33  2013   48.13        有         简装   \n",
       "17        东      驷马桥   1室1厅     21  2016   50.08        有         其他   \n",
       "18       东北     一品天下   3室1厅     28  2012   83.60        有         其他   \n",
       "19        南       紫荆   4室3厅      6  2002  137.64        无         其他   \n",
       "20      南 北     温江老城   3室2厅      7  2006  129.42        有         其他   \n",
       "21       西南      三圣乡   2室2厅     34  2009   86.85        有         其他   \n",
       "22       西北      猛追湾   4室2厅     22  2000  186.17        有         其他   \n",
       "23        北       中和   4室2厅     26  2013  143.03        有         其他   \n",
       "24    东南 西北       沙湾   2室2厅      7  2000   92.30        无         其他   \n",
       "25        南       华阳   3室1厅      6  2010  130.00        无         毛坯   \n",
       "26        东      万象城   3室2厅     18  2008  116.20        有         精装   \n",
       "27        东       大源   2室2厅     31  2014   89.05        有         精装   \n",
       "28       东北       成外   4室2厅     25  2012  115.73        有         其他   \n",
       "29       东北      橡树湾   2室1厅     25  2016   93.86        有         其他   \n",
       "30        北      沙河堡   3室2厅     34  2011   81.47        有         其他   \n",
       "...     ...      ...    ...    ...   ...     ...      ...        ...   \n",
       "2967     西南      猛追湾   2室1厅     39  2011   76.73        有         其他   \n",
       "2968     西南      茶店子   2室2厅     30  2009   75.85        有         其他   \n",
       "2969     东南     武侯立交   2室2厅     11  2004   78.53        有         毛坯   \n",
       "2970      东      新双楠   3室2厅     16  2011  113.02        有         毛坯   \n",
       "2971      西       南湖   4室2厅     26  2009  124.54        有         其他   \n",
       "2972     西北      外双楠   4室2厅     11  2006  273.91        有         精装   \n",
       "2974      南     东光小区   3室1厅      6  2003   82.30        无         毛坯   \n",
       "2975     西南       东苑   3室2厅     26  2009  135.00        有         简装   \n",
       "2976      西      外光华   3室2厅     21  2013   80.11        有         其他   \n",
       "2977     西北     人民公园   2室2厅     32  2009   88.21        有         简装   \n",
       "2978      东      驷马桥   2室2厅     21  2010   68.37        有         其他   \n",
       "2979     西南       南湖   4室2厅     34  2010  178.76        有         其他   \n",
       "2980     东南       国宾   2室1厅     30  2013   86.97        有         毛坯   \n",
       "2981     西南       国宾   3室2厅     26  2011   92.41        有         精装   \n",
       "2982    南 北       南湖   2室1厅      6  2002   90.43        无         毛坯   \n",
       "2983     东南      高升桥   2室1厅      7  2000   56.09        无         其他   \n",
       "2984     东南      茶店子   3室2厅      7  1999  131.58        无         其他   \n",
       "2985     东南      蜀汉路   2室1厅     12  2006   63.98        有         其他   \n",
       "2986   东 东北      驷马桥   3室2厅     18  2010  116.76        有         简装   \n",
       "2987      南     草金立交   4室2厅     11  2005  147.21        有         精装   \n",
       "2988      西       大源   4室2厅     31  2012  140.88        有         其他   \n",
       "2989      东       大源   3室1厅     29  2014  108.00        有         其他   \n",
       "2990      南      外双楠   2室1厅      9  2003   76.00        有         简装   \n",
       "2991      东     成渝立交   3室2厅     33  2008   97.36        有         其他   \n",
       "2992     东南       华阳   4室2厅      6  2003  157.13        无         其他   \n",
       "2993     东南       成外   2室2厅     18  2011   85.23        有         其他   \n",
       "2994     东北      万象城   2室1厅     28  2009   69.29        有         简装   \n",
       "2995     东南      神仙树   3室1厅      7  1995   83.84        无         毛坯   \n",
       "2996     东南      万象城   3室2厅     32  2009   89.84        有         简装   \n",
       "2997     东北     东郊记忆   3室2厅     33  2009   90.04        有         精装   \n",
       "\n",
       "      PerPrice   Price  \n",
       "1     1.830544   140.0  \n",
       "2     1.099404    72.0  \n",
       "3     1.135570   130.0  \n",
       "4     2.222498   179.0  \n",
       "5     1.468176   179.0  \n",
       "6     1.739001   117.0  \n",
       "7     1.240891   235.0  \n",
       "8     1.759101   216.0  \n",
       "9     1.356612   115.0  \n",
       "10    4.326961  1100.0  \n",
       "11    1.112701    70.0  \n",
       "12    1.638380   195.0  \n",
       "13    2.614721   200.0  \n",
       "14    1.894032   232.0  \n",
       "15    1.510094   190.0  \n",
       "16    1.246624    60.0  \n",
       "17    1.797125    90.0  \n",
       "18    2.870813   240.0  \n",
       "19    1.474862   203.0  \n",
       "20    0.904033   117.0  \n",
       "21    1.807714   157.0  \n",
       "22    1.987431   370.0  \n",
       "23    1.943648   278.0  \n",
       "24    1.300108   120.0  \n",
       "25    1.538462   200.0  \n",
       "26    1.936317   225.0  \n",
       "27    2.796182   249.0  \n",
       "28    1.728160   200.0  \n",
       "29    0.990837    93.0  \n",
       "30    2.062109   168.0  \n",
       "...        ...     ...  \n",
       "2967  2.020070   155.0  \n",
       "2968  2.043507   155.0  \n",
       "2969  1.171527    92.0  \n",
       "2970  2.265086   256.0  \n",
       "2971  1.365023   170.0  \n",
       "2972  2.555584   700.0  \n",
       "2974  1.336574   110.0  \n",
       "2975  1.837037   248.0  \n",
       "2976  2.059668   165.0  \n",
       "2977  2.324000   205.0  \n",
       "2978  1.608893   110.0  \n",
       "2979  1.510405   270.0  \n",
       "2980  1.655743   144.0  \n",
       "2981  1.698950   157.0  \n",
       "2982  1.017361    92.0  \n",
       "2983  1.230166    69.0  \n",
       "2984  1.367989   180.0  \n",
       "2985  1.594248   102.0  \n",
       "2986  1.301816   152.0  \n",
       "2987  1.358603   200.0  \n",
       "2988  2.519875   355.0  \n",
       "2989  2.083333   225.0  \n",
       "2990  1.684211   128.0  \n",
       "2991  1.869351   182.0  \n",
       "2992  1.463756   230.0  \n",
       "2993  1.290625   110.0  \n",
       "2994  1.804012   125.0  \n",
       "2995  1.097328    92.0  \n",
       "2996  2.215049   199.0  \n",
       "2997  1.754776   158.0  \n",
       "\n",
       "[2784 rows x 10 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program\\python\\lib\\site-packages\\pandas\\core\\frame.py:3140: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "df[['Layout_room','Layout_hall']] = df['Layout'].str.extract('(\\d)室(\\d)厅').astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program\\python\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['Year'] = pd.qcut(df['Year'],8).astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>District</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Year</th>\n",
       "      <th>Size</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Renovation</th>\n",
       "      <th>PerPrice</th>\n",
       "      <th>Price</th>\n",
       "      <th>Layout_room</th>\n",
       "      <th>Layout_hall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>东</td>\n",
       "      <td>南湖</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>34</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>76.48</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.830544</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>西南</td>\n",
       "      <td>水碾河</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>65.49</td>\n",
       "      <td>无</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.099404</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东南</td>\n",
       "      <td>成外</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>18</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>114.48</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.135570</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>南</td>\n",
       "      <td>万象城</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>33</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>80.54</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>2.222498</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>东南</td>\n",
       "      <td>成外</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>16</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>121.92</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.468176</td>\n",
       "      <td>179.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>西南</td>\n",
       "      <td>衣冠庙</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>15</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>67.28</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.739001</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>东南</td>\n",
       "      <td>营门口</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>189.38</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.240891</td>\n",
       "      <td>235.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>东南</td>\n",
       "      <td>红牌楼</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>122.79</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.759101</td>\n",
       "      <td>216.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>南</td>\n",
       "      <td>光华大道沿线</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>34</td>\n",
       "      <td>(2013.0, 2014.0]</td>\n",
       "      <td>84.77</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.356612</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>南 北</td>\n",
       "      <td>金融城</td>\n",
       "      <td>5室2厅</td>\n",
       "      <td>30</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>254.22</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>4.326961</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>东南</td>\n",
       "      <td>龙泉驿城区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>21</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>62.91</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.112701</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>南</td>\n",
       "      <td>蓝谷地</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>11</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>119.02</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.638380</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>南</td>\n",
       "      <td>大源</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>31</td>\n",
       "      <td>(2013.0, 2014.0]</td>\n",
       "      <td>76.49</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.614721</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>东</td>\n",
       "      <td>建设路</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>122.49</td>\n",
       "      <td>无</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.894032</td>\n",
       "      <td>232.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>北</td>\n",
       "      <td>广都</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>18</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>125.82</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.510094</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>南 北</td>\n",
       "      <td>橡树湾</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>33</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>48.13</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.246624</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>东</td>\n",
       "      <td>驷马桥</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>21</td>\n",
       "      <td>(2014.0, 2018.0]</td>\n",
       "      <td>50.08</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.797125</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>东北</td>\n",
       "      <td>一品天下</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>28</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>83.60</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.870813</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>南</td>\n",
       "      <td>紫荆</td>\n",
       "      <td>4室3厅</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>137.64</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.474862</td>\n",
       "      <td>203.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>南 北</td>\n",
       "      <td>温江老城</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>129.42</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>0.904033</td>\n",
       "      <td>117.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>西南</td>\n",
       "      <td>三圣乡</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>34</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>86.85</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.807714</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>西北</td>\n",
       "      <td>猛追湾</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>22</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>186.17</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.987431</td>\n",
       "      <td>370.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>北</td>\n",
       "      <td>中和</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>26</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>143.03</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.943648</td>\n",
       "      <td>278.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>东南 西北</td>\n",
       "      <td>沙湾</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>92.30</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.300108</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>南</td>\n",
       "      <td>华阳</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>6</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>130.00</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.538462</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>东</td>\n",
       "      <td>万象城</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>18</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>116.20</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.936317</td>\n",
       "      <td>225.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>东</td>\n",
       "      <td>大源</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>31</td>\n",
       "      <td>(2013.0, 2014.0]</td>\n",
       "      <td>89.05</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>2.796182</td>\n",
       "      <td>249.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>东北</td>\n",
       "      <td>成外</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>25</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>115.73</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.728160</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>东北</td>\n",
       "      <td>橡树湾</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>25</td>\n",
       "      <td>(2014.0, 2018.0]</td>\n",
       "      <td>93.86</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>0.990837</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>北</td>\n",
       "      <td>沙河堡</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>34</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>81.47</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.062109</td>\n",
       "      <td>168.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>西南</td>\n",
       "      <td>猛追湾</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>39</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>76.73</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.020070</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>西南</td>\n",
       "      <td>茶店子</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>30</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>75.85</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.043507</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2969</th>\n",
       "      <td>东南</td>\n",
       "      <td>武侯立交</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>11</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>78.53</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.171527</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2970</th>\n",
       "      <td>东</td>\n",
       "      <td>新双楠</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>16</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>113.02</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>2.265086</td>\n",
       "      <td>256.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>西</td>\n",
       "      <td>南湖</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>26</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>124.54</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.365023</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2972</th>\n",
       "      <td>西北</td>\n",
       "      <td>外双楠</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>11</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>273.91</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>2.555584</td>\n",
       "      <td>700.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2974</th>\n",
       "      <td>南</td>\n",
       "      <td>东光小区</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>82.30</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.336574</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>西南</td>\n",
       "      <td>东苑</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>26</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>135.00</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.837037</td>\n",
       "      <td>248.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>西</td>\n",
       "      <td>外光华</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>21</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>80.11</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.059668</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>西北</td>\n",
       "      <td>人民公园</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>32</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>88.21</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>2.324000</td>\n",
       "      <td>205.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2978</th>\n",
       "      <td>东</td>\n",
       "      <td>驷马桥</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>21</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>68.37</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.608893</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979</th>\n",
       "      <td>西南</td>\n",
       "      <td>南湖</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>34</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>178.76</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.510405</td>\n",
       "      <td>270.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2980</th>\n",
       "      <td>东南</td>\n",
       "      <td>国宾</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>30</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>86.97</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.655743</td>\n",
       "      <td>144.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2981</th>\n",
       "      <td>西南</td>\n",
       "      <td>国宾</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>26</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>92.41</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.698950</td>\n",
       "      <td>157.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>南 北</td>\n",
       "      <td>南湖</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>90.43</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.017361</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>东南</td>\n",
       "      <td>高升桥</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>56.09</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.230166</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>东南</td>\n",
       "      <td>茶店子</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>131.58</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.367989</td>\n",
       "      <td>180.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2985</th>\n",
       "      <td>东南</td>\n",
       "      <td>蜀汉路</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>12</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>63.98</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.594248</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>东 东北</td>\n",
       "      <td>驷马桥</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>18</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>116.76</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.301816</td>\n",
       "      <td>152.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>南</td>\n",
       "      <td>草金立交</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>11</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>147.21</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.358603</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>西</td>\n",
       "      <td>大源</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>31</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>140.88</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.519875</td>\n",
       "      <td>355.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989</th>\n",
       "      <td>东</td>\n",
       "      <td>大源</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>29</td>\n",
       "      <td>(2013.0, 2014.0]</td>\n",
       "      <td>108.00</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>2.083333</td>\n",
       "      <td>225.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>南</td>\n",
       "      <td>外双楠</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>9</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>76.00</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.684211</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2991</th>\n",
       "      <td>东</td>\n",
       "      <td>成渝立交</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>33</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>97.36</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.869351</td>\n",
       "      <td>182.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>东南</td>\n",
       "      <td>华阳</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>157.13</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.463756</td>\n",
       "      <td>230.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>东南</td>\n",
       "      <td>成外</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>18</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>85.23</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1.290625</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>东北</td>\n",
       "      <td>万象城</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>28</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>69.29</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>1.804012</td>\n",
       "      <td>125.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>东南</td>\n",
       "      <td>神仙树</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>83.84</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>1.097328</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>东南</td>\n",
       "      <td>万象城</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>32</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>89.84</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>2.215049</td>\n",
       "      <td>199.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>东北</td>\n",
       "      <td>东郊记忆</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>33</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>90.04</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>1.754776</td>\n",
       "      <td>158.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2784 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region District Layout  Floor                Year    Size Elevator  \\\n",
       "1         东       南湖   2室1厅     34    (2009.0, 2010.0]   76.48        有   \n",
       "2        西南      水碾河   2室1厅      6  (1979.999, 2003.0]   65.49        无   \n",
       "3        东南       成外   3室1厅     18    (2010.0, 2012.0]  114.48        有   \n",
       "4         南      万象城   2室1厅     33    (2010.0, 2012.0]   80.54        有   \n",
       "5        东南       成外   3室2厅     16    (2010.0, 2012.0]  121.92        有   \n",
       "6        西南      衣冠庙   2室1厅     15    (2003.0, 2007.0]   67.28        有   \n",
       "7        东南      营门口   4室2厅      6  (1979.999, 2003.0]  189.38        无   \n",
       "8        东南      红牌楼   3室2厅      7  (1979.999, 2003.0]  122.79        无   \n",
       "9         南   光华大道沿线   3室1厅     34    (2013.0, 2014.0]   84.77        有   \n",
       "10      南 北      金融城   5室2厅     30    (2012.0, 2013.0]  254.22        有   \n",
       "11       东南    龙泉驿城区   2室1厅     21    (2003.0, 2007.0]   62.91        有   \n",
       "12        南      蓝谷地   3室2厅     11    (2003.0, 2007.0]  119.02        有   \n",
       "13        南       大源   2室2厅     31    (2013.0, 2014.0]   76.49        有   \n",
       "14        东      建设路   3室2厅      7  (1979.999, 2003.0]  122.49        无   \n",
       "15        北       广都   3室2厅     18    (2003.0, 2007.0]  125.82        有   \n",
       "16      南 北      橡树湾   2室2厅     33    (2012.0, 2013.0]   48.13        有   \n",
       "17        东      驷马桥   1室1厅     21    (2014.0, 2018.0]   50.08        有   \n",
       "18       东北     一品天下   3室1厅     28    (2010.0, 2012.0]   83.60        有   \n",
       "19        南       紫荆   4室3厅      6  (1979.999, 2003.0]  137.64        无   \n",
       "20      南 北     温江老城   3室2厅      7    (2003.0, 2007.0]  129.42        有   \n",
       "21       西南      三圣乡   2室2厅     34    (2007.0, 2009.0]   86.85        有   \n",
       "22       西北      猛追湾   4室2厅     22  (1979.999, 2003.0]  186.17        有   \n",
       "23        北       中和   4室2厅     26    (2012.0, 2013.0]  143.03        有   \n",
       "24    东南 西北       沙湾   2室2厅      7  (1979.999, 2003.0]   92.30        无   \n",
       "25        南       华阳   3室1厅      6    (2009.0, 2010.0]  130.00        无   \n",
       "26        东      万象城   3室2厅     18    (2007.0, 2009.0]  116.20        有   \n",
       "27        东       大源   2室2厅     31    (2013.0, 2014.0]   89.05        有   \n",
       "28       东北       成外   4室2厅     25    (2010.0, 2012.0]  115.73        有   \n",
       "29       东北      橡树湾   2室1厅     25    (2014.0, 2018.0]   93.86        有   \n",
       "30        北      沙河堡   3室2厅     34    (2010.0, 2012.0]   81.47        有   \n",
       "...     ...      ...    ...    ...                 ...     ...      ...   \n",
       "2967     西南      猛追湾   2室1厅     39    (2010.0, 2012.0]   76.73        有   \n",
       "2968     西南      茶店子   2室2厅     30    (2007.0, 2009.0]   75.85        有   \n",
       "2969     东南     武侯立交   2室2厅     11    (2003.0, 2007.0]   78.53        有   \n",
       "2970      东      新双楠   3室2厅     16    (2010.0, 2012.0]  113.02        有   \n",
       "2971      西       南湖   4室2厅     26    (2007.0, 2009.0]  124.54        有   \n",
       "2972     西北      外双楠   4室2厅     11    (2003.0, 2007.0]  273.91        有   \n",
       "2974      南     东光小区   3室1厅      6  (1979.999, 2003.0]   82.30        无   \n",
       "2975     西南       东苑   3室2厅     26    (2007.0, 2009.0]  135.00        有   \n",
       "2976      西      外光华   3室2厅     21    (2012.0, 2013.0]   80.11        有   \n",
       "2977     西北     人民公园   2室2厅     32    (2007.0, 2009.0]   88.21        有   \n",
       "2978      东      驷马桥   2室2厅     21    (2009.0, 2010.0]   68.37        有   \n",
       "2979     西南       南湖   4室2厅     34    (2009.0, 2010.0]  178.76        有   \n",
       "2980     东南       国宾   2室1厅     30    (2012.0, 2013.0]   86.97        有   \n",
       "2981     西南       国宾   3室2厅     26    (2010.0, 2012.0]   92.41        有   \n",
       "2982    南 北       南湖   2室1厅      6  (1979.999, 2003.0]   90.43        无   \n",
       "2983     东南      高升桥   2室1厅      7  (1979.999, 2003.0]   56.09        无   \n",
       "2984     东南      茶店子   3室2厅      7  (1979.999, 2003.0]  131.58        无   \n",
       "2985     东南      蜀汉路   2室1厅     12    (2003.0, 2007.0]   63.98        有   \n",
       "2986   东 东北      驷马桥   3室2厅     18    (2009.0, 2010.0]  116.76        有   \n",
       "2987      南     草金立交   4室2厅     11    (2003.0, 2007.0]  147.21        有   \n",
       "2988      西       大源   4室2厅     31    (2010.0, 2012.0]  140.88        有   \n",
       "2989      东       大源   3室1厅     29    (2013.0, 2014.0]  108.00        有   \n",
       "2990      南      外双楠   2室1厅      9  (1979.999, 2003.0]   76.00        有   \n",
       "2991      东     成渝立交   3室2厅     33    (2007.0, 2009.0]   97.36        有   \n",
       "2992     东南       华阳   4室2厅      6  (1979.999, 2003.0]  157.13        无   \n",
       "2993     东南       成外   2室2厅     18    (2010.0, 2012.0]   85.23        有   \n",
       "2994     东北      万象城   2室1厅     28    (2007.0, 2009.0]   69.29        有   \n",
       "2995     东南      神仙树   3室1厅      7  (1979.999, 2003.0]   83.84        无   \n",
       "2996     东南      万象城   3室2厅     32    (2007.0, 2009.0]   89.84        有   \n",
       "2997     东北     东郊记忆   3室2厅     33    (2007.0, 2009.0]   90.04        有   \n",
       "\n",
       "     Renovation  PerPrice   Price  Layout_room  Layout_hall  \n",
       "1            其他  1.830544   140.0            2            1  \n",
       "2            简装  1.099404    72.0            2            1  \n",
       "3            其他  1.135570   130.0            3            1  \n",
       "4            简装  2.222498   179.0            2            1  \n",
       "5            精装  1.468176   179.0            3            2  \n",
       "6            简装  1.739001   117.0            2            1  \n",
       "7            其他  1.240891   235.0            4            2  \n",
       "8            其他  1.759101   216.0            3            2  \n",
       "9            毛坯  1.356612   115.0            3            1  \n",
       "10           其他  4.326961  1100.0            5            2  \n",
       "11           其他  1.112701    70.0            2            1  \n",
       "12           其他  1.638380   195.0            3            2  \n",
       "13           其他  2.614721   200.0            2            2  \n",
       "14           简装  1.894032   232.0            3            2  \n",
       "15           其他  1.510094   190.0            3            2  \n",
       "16           简装  1.246624    60.0            2            2  \n",
       "17           其他  1.797125    90.0            1            1  \n",
       "18           其他  2.870813   240.0            3            1  \n",
       "19           其他  1.474862   203.0            4            3  \n",
       "20           其他  0.904033   117.0            3            2  \n",
       "21           其他  1.807714   157.0            2            2  \n",
       "22           其他  1.987431   370.0            4            2  \n",
       "23           其他  1.943648   278.0            4            2  \n",
       "24           其他  1.300108   120.0            2            2  \n",
       "25           毛坯  1.538462   200.0            3            1  \n",
       "26           精装  1.936317   225.0            3            2  \n",
       "27           精装  2.796182   249.0            2            2  \n",
       "28           其他  1.728160   200.0            4            2  \n",
       "29           其他  0.990837    93.0            2            1  \n",
       "30           其他  2.062109   168.0            3            2  \n",
       "...         ...       ...     ...          ...          ...  \n",
       "2967         其他  2.020070   155.0            2            1  \n",
       "2968         其他  2.043507   155.0            2            2  \n",
       "2969         毛坯  1.171527    92.0            2            2  \n",
       "2970         毛坯  2.265086   256.0            3            2  \n",
       "2971         其他  1.365023   170.0            4            2  \n",
       "2972         精装  2.555584   700.0            4            2  \n",
       "2974         毛坯  1.336574   110.0            3            1  \n",
       "2975         简装  1.837037   248.0            3            2  \n",
       "2976         其他  2.059668   165.0            3            2  \n",
       "2977         简装  2.324000   205.0            2            2  \n",
       "2978         其他  1.608893   110.0            2            2  \n",
       "2979         其他  1.510405   270.0            4            2  \n",
       "2980         毛坯  1.655743   144.0            2            1  \n",
       "2981         精装  1.698950   157.0            3            2  \n",
       "2982         毛坯  1.017361    92.0            2            1  \n",
       "2983         其他  1.230166    69.0            2            1  \n",
       "2984         其他  1.367989   180.0            3            2  \n",
       "2985         其他  1.594248   102.0            2            1  \n",
       "2986         简装  1.301816   152.0            3            2  \n",
       "2987         精装  1.358603   200.0            4            2  \n",
       "2988         其他  2.519875   355.0            4            2  \n",
       "2989         其他  2.083333   225.0            3            1  \n",
       "2990         简装  1.684211   128.0            2            1  \n",
       "2991         其他  1.869351   182.0            3            2  \n",
       "2992         其他  1.463756   230.0            4            2  \n",
       "2993         其他  1.290625   110.0            2            2  \n",
       "2994         简装  1.804012   125.0            2            1  \n",
       "2995         毛坯  1.097328    92.0            3            1  \n",
       "2996         简装  2.215049   199.0            3            2  \n",
       "2997         精装  1.754776   158.0            3            2  \n",
       "\n",
       "[2784 rows x 12 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "南         633\n",
       "东南        605\n",
       "西南        314\n",
       "东         287\n",
       "北         201\n",
       "东北        179\n",
       "西北        160\n",
       "西         155\n",
       "南 北       115\n",
       "东 西        29\n",
       "东南 西北      18\n",
       "西南 东北      16\n",
       "东南 南       14\n",
       "东 东南        7\n",
       "东南 西南       6\n",
       "东 南         5\n",
       "南 西         5\n",
       "南 西南        4\n",
       "西南 西        3\n",
       "东 北         3\n",
       "西南 西北       3\n",
       "西 西北        3\n",
       "东南 东北       3\n",
       "北 东北        3\n",
       "东南 西        3\n",
       "南 西北        2\n",
       "东 东北        2\n",
       "西 北         1\n",
       "西南 南        1\n",
       "西北 东北       1\n",
       "东 西北        1\n",
       "东 北 东北      1\n",
       "东南 北        1\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program\\python\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "d:\\program\\python\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# 根据已有特征创建新特征\n",
    "df['Layout_total'] = df['Layout_room'] + df['Layout_hall']\n",
    "df['Size_room_ratio'] = df['Size']/df['Layout_total']\n",
    "\n",
    "# 删除无用特征\n",
    "df = df.drop(['Layout','PerPrice'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>District</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Year</th>\n",
       "      <th>Size</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Renovation</th>\n",
       "      <th>Price</th>\n",
       "      <th>Layout_room</th>\n",
       "      <th>Layout_hall</th>\n",
       "      <th>Layout_total</th>\n",
       "      <th>Size_room_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>东</td>\n",
       "      <td>南湖</td>\n",
       "      <td>34</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>76.48</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25.493333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>西南</td>\n",
       "      <td>水碾河</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>65.49</td>\n",
       "      <td>无</td>\n",
       "      <td>简装</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>21.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东南</td>\n",
       "      <td>成外</td>\n",
       "      <td>18</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>114.48</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>28.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>南</td>\n",
       "      <td>万象城</td>\n",
       "      <td>33</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>80.54</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.846667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>东南</td>\n",
       "      <td>成外</td>\n",
       "      <td>16</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>121.92</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>179.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24.384000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>西南</td>\n",
       "      <td>衣冠庙</td>\n",
       "      <td>15</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>67.28</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22.426667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>东南</td>\n",
       "      <td>营门口</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>189.38</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>235.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>31.563333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>东南</td>\n",
       "      <td>红牌楼</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>122.79</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>216.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24.558000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>南</td>\n",
       "      <td>光华大道沿线</td>\n",
       "      <td>34</td>\n",
       "      <td>(2013.0, 2014.0]</td>\n",
       "      <td>84.77</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>21.192500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>南 北</td>\n",
       "      <td>金融城</td>\n",
       "      <td>30</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>254.22</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>36.317143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>东南</td>\n",
       "      <td>龙泉驿城区</td>\n",
       "      <td>21</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>62.91</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>南</td>\n",
       "      <td>蓝谷地</td>\n",
       "      <td>11</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>119.02</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23.804000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>南</td>\n",
       "      <td>大源</td>\n",
       "      <td>31</td>\n",
       "      <td>(2013.0, 2014.0]</td>\n",
       "      <td>76.49</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19.122500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>东</td>\n",
       "      <td>建设路</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>122.49</td>\n",
       "      <td>无</td>\n",
       "      <td>简装</td>\n",
       "      <td>232.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24.498000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>北</td>\n",
       "      <td>广都</td>\n",
       "      <td>18</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>125.82</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>25.164000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>南 北</td>\n",
       "      <td>橡树湾</td>\n",
       "      <td>33</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>48.13</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>12.032500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>东</td>\n",
       "      <td>驷马桥</td>\n",
       "      <td>21</td>\n",
       "      <td>(2014.0, 2018.0]</td>\n",
       "      <td>50.08</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>东北</td>\n",
       "      <td>一品天下</td>\n",
       "      <td>28</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>83.60</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>南</td>\n",
       "      <td>紫荆</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>137.64</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>203.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>19.662857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>南 北</td>\n",
       "      <td>温江老城</td>\n",
       "      <td>7</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>129.42</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>117.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>25.884000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>西南</td>\n",
       "      <td>三圣乡</td>\n",
       "      <td>34</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>86.85</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>21.712500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>西北</td>\n",
       "      <td>猛追湾</td>\n",
       "      <td>22</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>186.17</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>370.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>31.028333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>北</td>\n",
       "      <td>中和</td>\n",
       "      <td>26</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>143.03</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>278.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>23.838333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>东南 西北</td>\n",
       "      <td>沙湾</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>92.30</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>23.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>南</td>\n",
       "      <td>华阳</td>\n",
       "      <td>6</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>130.00</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>32.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>东</td>\n",
       "      <td>万象城</td>\n",
       "      <td>18</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>116.20</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>225.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>东</td>\n",
       "      <td>大源</td>\n",
       "      <td>31</td>\n",
       "      <td>(2013.0, 2014.0]</td>\n",
       "      <td>89.05</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>249.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>22.262500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>东北</td>\n",
       "      <td>成外</td>\n",
       "      <td>25</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>115.73</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19.288333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>东北</td>\n",
       "      <td>橡树湾</td>\n",
       "      <td>25</td>\n",
       "      <td>(2014.0, 2018.0]</td>\n",
       "      <td>93.86</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>31.286667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>北</td>\n",
       "      <td>沙河堡</td>\n",
       "      <td>34</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>81.47</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>168.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>16.294000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>西南</td>\n",
       "      <td>猛追湾</td>\n",
       "      <td>39</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>76.73</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25.576667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>西南</td>\n",
       "      <td>茶店子</td>\n",
       "      <td>30</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>75.85</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>18.962500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2969</th>\n",
       "      <td>东南</td>\n",
       "      <td>武侯立交</td>\n",
       "      <td>11</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>78.53</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19.632500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2970</th>\n",
       "      <td>东</td>\n",
       "      <td>新双楠</td>\n",
       "      <td>16</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>113.02</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>256.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>22.604000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>西</td>\n",
       "      <td>南湖</td>\n",
       "      <td>26</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>124.54</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>20.756667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2972</th>\n",
       "      <td>西北</td>\n",
       "      <td>外双楠</td>\n",
       "      <td>11</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>273.91</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>700.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>45.651667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2974</th>\n",
       "      <td>南</td>\n",
       "      <td>东光小区</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>82.30</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>西南</td>\n",
       "      <td>东苑</td>\n",
       "      <td>26</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>135.00</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>248.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>西</td>\n",
       "      <td>外光华</td>\n",
       "      <td>21</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>80.11</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>16.022000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>西北</td>\n",
       "      <td>人民公园</td>\n",
       "      <td>32</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>88.21</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>205.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>22.052500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2978</th>\n",
       "      <td>东</td>\n",
       "      <td>驷马桥</td>\n",
       "      <td>21</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>68.37</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>17.092500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979</th>\n",
       "      <td>西南</td>\n",
       "      <td>南湖</td>\n",
       "      <td>34</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>178.76</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>270.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>29.793333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2980</th>\n",
       "      <td>东南</td>\n",
       "      <td>国宾</td>\n",
       "      <td>30</td>\n",
       "      <td>(2012.0, 2013.0]</td>\n",
       "      <td>86.97</td>\n",
       "      <td>有</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>144.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>28.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2981</th>\n",
       "      <td>西南</td>\n",
       "      <td>国宾</td>\n",
       "      <td>26</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>92.41</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>157.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>18.482000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>南 北</td>\n",
       "      <td>南湖</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>90.43</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30.143333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>东南</td>\n",
       "      <td>高升桥</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>56.09</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18.696667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>东南</td>\n",
       "      <td>茶店子</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>131.58</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>180.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>26.316000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2985</th>\n",
       "      <td>东南</td>\n",
       "      <td>蜀汉路</td>\n",
       "      <td>12</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>63.98</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>21.326667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>东 东北</td>\n",
       "      <td>驷马桥</td>\n",
       "      <td>18</td>\n",
       "      <td>(2009.0, 2010.0]</td>\n",
       "      <td>116.76</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>152.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23.352000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>南</td>\n",
       "      <td>草金立交</td>\n",
       "      <td>11</td>\n",
       "      <td>(2003.0, 2007.0]</td>\n",
       "      <td>147.21</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>24.535000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>西</td>\n",
       "      <td>大源</td>\n",
       "      <td>31</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>140.88</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>355.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>23.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989</th>\n",
       "      <td>东</td>\n",
       "      <td>大源</td>\n",
       "      <td>29</td>\n",
       "      <td>(2013.0, 2014.0]</td>\n",
       "      <td>108.00</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>225.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>南</td>\n",
       "      <td>外双楠</td>\n",
       "      <td>9</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>76.00</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2991</th>\n",
       "      <td>东</td>\n",
       "      <td>成渝立交</td>\n",
       "      <td>33</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>97.36</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>182.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>19.472000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>东南</td>\n",
       "      <td>华阳</td>\n",
       "      <td>6</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>157.13</td>\n",
       "      <td>无</td>\n",
       "      <td>其他</td>\n",
       "      <td>230.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>26.188333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>东南</td>\n",
       "      <td>成外</td>\n",
       "      <td>18</td>\n",
       "      <td>(2010.0, 2012.0]</td>\n",
       "      <td>85.23</td>\n",
       "      <td>有</td>\n",
       "      <td>其他</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>21.307500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>东北</td>\n",
       "      <td>万象城</td>\n",
       "      <td>28</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>69.29</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>125.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>23.096667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>东南</td>\n",
       "      <td>神仙树</td>\n",
       "      <td>7</td>\n",
       "      <td>(1979.999, 2003.0]</td>\n",
       "      <td>83.84</td>\n",
       "      <td>无</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>东南</td>\n",
       "      <td>万象城</td>\n",
       "      <td>32</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>89.84</td>\n",
       "      <td>有</td>\n",
       "      <td>简装</td>\n",
       "      <td>199.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>17.968000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>东北</td>\n",
       "      <td>东郊记忆</td>\n",
       "      <td>33</td>\n",
       "      <td>(2007.0, 2009.0]</td>\n",
       "      <td>90.04</td>\n",
       "      <td>有</td>\n",
       "      <td>精装</td>\n",
       "      <td>158.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>18.008000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2784 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region District  Floor                Year    Size Elevator Renovation  \\\n",
       "1         东       南湖     34    (2009.0, 2010.0]   76.48        有         其他   \n",
       "2        西南      水碾河      6  (1979.999, 2003.0]   65.49        无         简装   \n",
       "3        东南       成外     18    (2010.0, 2012.0]  114.48        有         其他   \n",
       "4         南      万象城     33    (2010.0, 2012.0]   80.54        有         简装   \n",
       "5        东南       成外     16    (2010.0, 2012.0]  121.92        有         精装   \n",
       "6        西南      衣冠庙     15    (2003.0, 2007.0]   67.28        有         简装   \n",
       "7        东南      营门口      6  (1979.999, 2003.0]  189.38        无         其他   \n",
       "8        东南      红牌楼      7  (1979.999, 2003.0]  122.79        无         其他   \n",
       "9         南   光华大道沿线     34    (2013.0, 2014.0]   84.77        有         毛坯   \n",
       "10      南 北      金融城     30    (2012.0, 2013.0]  254.22        有         其他   \n",
       "11       东南    龙泉驿城区     21    (2003.0, 2007.0]   62.91        有         其他   \n",
       "12        南      蓝谷地     11    (2003.0, 2007.0]  119.02        有         其他   \n",
       "13        南       大源     31    (2013.0, 2014.0]   76.49        有         其他   \n",
       "14        东      建设路      7  (1979.999, 2003.0]  122.49        无         简装   \n",
       "15        北       广都     18    (2003.0, 2007.0]  125.82        有         其他   \n",
       "16      南 北      橡树湾     33    (2012.0, 2013.0]   48.13        有         简装   \n",
       "17        东      驷马桥     21    (2014.0, 2018.0]   50.08        有         其他   \n",
       "18       东北     一品天下     28    (2010.0, 2012.0]   83.60        有         其他   \n",
       "19        南       紫荆      6  (1979.999, 2003.0]  137.64        无         其他   \n",
       "20      南 北     温江老城      7    (2003.0, 2007.0]  129.42        有         其他   \n",
       "21       西南      三圣乡     34    (2007.0, 2009.0]   86.85        有         其他   \n",
       "22       西北      猛追湾     22  (1979.999, 2003.0]  186.17        有         其他   \n",
       "23        北       中和     26    (2012.0, 2013.0]  143.03        有         其他   \n",
       "24    东南 西北       沙湾      7  (1979.999, 2003.0]   92.30        无         其他   \n",
       "25        南       华阳      6    (2009.0, 2010.0]  130.00        无         毛坯   \n",
       "26        东      万象城     18    (2007.0, 2009.0]  116.20        有         精装   \n",
       "27        东       大源     31    (2013.0, 2014.0]   89.05        有         精装   \n",
       "28       东北       成外     25    (2010.0, 2012.0]  115.73        有         其他   \n",
       "29       东北      橡树湾     25    (2014.0, 2018.0]   93.86        有         其他   \n",
       "30        北      沙河堡     34    (2010.0, 2012.0]   81.47        有         其他   \n",
       "...     ...      ...    ...                 ...     ...      ...        ...   \n",
       "2967     西南      猛追湾     39    (2010.0, 2012.0]   76.73        有         其他   \n",
       "2968     西南      茶店子     30    (2007.0, 2009.0]   75.85        有         其他   \n",
       "2969     东南     武侯立交     11    (2003.0, 2007.0]   78.53        有         毛坯   \n",
       "2970      东      新双楠     16    (2010.0, 2012.0]  113.02        有         毛坯   \n",
       "2971      西       南湖     26    (2007.0, 2009.0]  124.54        有         其他   \n",
       "2972     西北      外双楠     11    (2003.0, 2007.0]  273.91        有         精装   \n",
       "2974      南     东光小区      6  (1979.999, 2003.0]   82.30        无         毛坯   \n",
       "2975     西南       东苑     26    (2007.0, 2009.0]  135.00        有         简装   \n",
       "2976      西      外光华     21    (2012.0, 2013.0]   80.11        有         其他   \n",
       "2977     西北     人民公园     32    (2007.0, 2009.0]   88.21        有         简装   \n",
       "2978      东      驷马桥     21    (2009.0, 2010.0]   68.37        有         其他   \n",
       "2979     西南       南湖     34    (2009.0, 2010.0]  178.76        有         其他   \n",
       "2980     东南       国宾     30    (2012.0, 2013.0]   86.97        有         毛坯   \n",
       "2981     西南       国宾     26    (2010.0, 2012.0]   92.41        有         精装   \n",
       "2982    南 北       南湖      6  (1979.999, 2003.0]   90.43        无         毛坯   \n",
       "2983     东南      高升桥      7  (1979.999, 2003.0]   56.09        无         其他   \n",
       "2984     东南      茶店子      7  (1979.999, 2003.0]  131.58        无         其他   \n",
       "2985     东南      蜀汉路     12    (2003.0, 2007.0]   63.98        有         其他   \n",
       "2986   东 东北      驷马桥     18    (2009.0, 2010.0]  116.76        有         简装   \n",
       "2987      南     草金立交     11    (2003.0, 2007.0]  147.21        有         精装   \n",
       "2988      西       大源     31    (2010.0, 2012.0]  140.88        有         其他   \n",
       "2989      东       大源     29    (2013.0, 2014.0]  108.00        有         其他   \n",
       "2990      南      外双楠      9  (1979.999, 2003.0]   76.00        有         简装   \n",
       "2991      东     成渝立交     33    (2007.0, 2009.0]   97.36        有         其他   \n",
       "2992     东南       华阳      6  (1979.999, 2003.0]  157.13        无         其他   \n",
       "2993     东南       成外     18    (2010.0, 2012.0]   85.23        有         其他   \n",
       "2994     东北      万象城     28    (2007.0, 2009.0]   69.29        有         简装   \n",
       "2995     东南      神仙树      7  (1979.999, 2003.0]   83.84        无         毛坯   \n",
       "2996     东南      万象城     32    (2007.0, 2009.0]   89.84        有         简装   \n",
       "2997     东北     东郊记忆     33    (2007.0, 2009.0]   90.04        有         精装   \n",
       "\n",
       "       Price  Layout_room  Layout_hall  Layout_total  Size_room_ratio  \n",
       "1      140.0            2            1             3        25.493333  \n",
       "2       72.0            2            1             3        21.830000  \n",
       "3      130.0            3            1             4        28.620000  \n",
       "4      179.0            2            1             3        26.846667  \n",
       "5      179.0            3            2             5        24.384000  \n",
       "6      117.0            2            1             3        22.426667  \n",
       "7      235.0            4            2             6        31.563333  \n",
       "8      216.0            3            2             5        24.558000  \n",
       "9      115.0            3            1             4        21.192500  \n",
       "10    1100.0            5            2             7        36.317143  \n",
       "11      70.0            2            1             3        20.970000  \n",
       "12     195.0            3            2             5        23.804000  \n",
       "13     200.0            2            2             4        19.122500  \n",
       "14     232.0            3            2             5        24.498000  \n",
       "15     190.0            3            2             5        25.164000  \n",
       "16      60.0            2            2             4        12.032500  \n",
       "17      90.0            1            1             2        25.040000  \n",
       "18     240.0            3            1             4        20.900000  \n",
       "19     203.0            4            3             7        19.662857  \n",
       "20     117.0            3            2             5        25.884000  \n",
       "21     157.0            2            2             4        21.712500  \n",
       "22     370.0            4            2             6        31.028333  \n",
       "23     278.0            4            2             6        23.838333  \n",
       "24     120.0            2            2             4        23.075000  \n",
       "25     200.0            3            1             4        32.500000  \n",
       "26     225.0            3            2             5        23.240000  \n",
       "27     249.0            2            2             4        22.262500  \n",
       "28     200.0            4            2             6        19.288333  \n",
       "29      93.0            2            1             3        31.286667  \n",
       "30     168.0            3            2             5        16.294000  \n",
       "...      ...          ...          ...           ...              ...  \n",
       "2967   155.0            2            1             3        25.576667  \n",
       "2968   155.0            2            2             4        18.962500  \n",
       "2969    92.0            2            2             4        19.632500  \n",
       "2970   256.0            3            2             5        22.604000  \n",
       "2971   170.0            4            2             6        20.756667  \n",
       "2972   700.0            4            2             6        45.651667  \n",
       "2974   110.0            3            1             4        20.575000  \n",
       "2975   248.0            3            2             5        27.000000  \n",
       "2976   165.0            3            2             5        16.022000  \n",
       "2977   205.0            2            2             4        22.052500  \n",
       "2978   110.0            2            2             4        17.092500  \n",
       "2979   270.0            4            2             6        29.793333  \n",
       "2980   144.0            2            1             3        28.990000  \n",
       "2981   157.0            3            2             5        18.482000  \n",
       "2982    92.0            2            1             3        30.143333  \n",
       "2983    69.0            2            1             3        18.696667  \n",
       "2984   180.0            3            2             5        26.316000  \n",
       "2985   102.0            2            1             3        21.326667  \n",
       "2986   152.0            3            2             5        23.352000  \n",
       "2987   200.0            4            2             6        24.535000  \n",
       "2988   355.0            4            2             6        23.480000  \n",
       "2989   225.0            3            1             4        27.000000  \n",
       "2990   128.0            2            1             3        25.333333  \n",
       "2991   182.0            3            2             5        19.472000  \n",
       "2992   230.0            4            2             6        26.188333  \n",
       "2993   110.0            2            2             4        21.307500  \n",
       "2994   125.0            2            1             3        23.096667  \n",
       "2995    92.0            3            1             4        20.960000  \n",
       "2996   199.0            3            2             5        17.968000  \n",
       "2997   158.0            3            2             5        18.008000  \n",
       "\n",
       "[2784 rows x 12 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Floor</th>\n",
       "      <th>Size</th>\n",
       "      <th>Price</th>\n",
       "      <th>Layout_room</th>\n",
       "      <th>Layout_hall</th>\n",
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       "      <th>Region_东</th>\n",
       "      <th>Region_东 东北</th>\n",
       "      <th>Region_东 东南</th>\n",
       "      <th>...</th>\n",
       "      <th>Year_(2010.0, 2012.0]</th>\n",
       "      <th>Year_(2012.0, 2013.0]</th>\n",
       "      <th>Year_(2013.0, 2014.0]</th>\n",
       "      <th>Year_(2014.0, 2018.0]</th>\n",
       "      <th>Elevator_无</th>\n",
       "      <th>Elevator_有</th>\n",
       "      <th>Renovation_其他</th>\n",
       "      <th>Renovation_毛坯</th>\n",
       "      <th>Renovation_简装</th>\n",
       "      <th>Renovation_精装</th>\n",
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       "      <th>1</th>\n",
       "      <td>34</td>\n",
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       "      <td>140.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>65.49</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>21.830000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>114.48</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>80.54</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.846667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>121.92</td>\n",
       "      <td>179.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24.384000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>67.28</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22.426667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>189.38</td>\n",
       "      <td>235.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>31.563333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>122.79</td>\n",
       "      <td>216.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24.558000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>34</td>\n",
       "      <td>84.77</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>21.192500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30</td>\n",
       "      <td>254.22</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>36.317143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21</td>\n",
       "      <td>62.91</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20.970000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>119.02</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23.804000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>31</td>\n",
       "      <td>76.49</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19.122500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>122.49</td>\n",
       "      <td>232.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24.498000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18</td>\n",
       "      <td>125.82</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>25.164000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>33</td>\n",
       "      <td>48.13</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>12.032500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>21</td>\n",
       "      <td>50.08</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25.040000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28</td>\n",
       "      <td>83.60</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6</td>\n",
       "      <td>137.64</td>\n",
       "      <td>203.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>19.662857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7</td>\n",
       "      <td>129.42</td>\n",
       "      <td>117.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>25.884000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34</td>\n",
       "      <td>86.85</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>21.712500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>186.17</td>\n",
       "      <td>370.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>31.028333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26</td>\n",
       "      <td>143.03</td>\n",
       "      <td>278.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>23.838333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7</td>\n",
       "      <td>92.30</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>23.075000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6</td>\n",
       "      <td>130.00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>18</td>\n",
       "      <td>116.20</td>\n",
       "      <td>225.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23.240000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31</td>\n",
       "      <td>89.05</td>\n",
       "      <td>249.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>22.262500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>25</td>\n",
       "      <td>115.73</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19.288333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>25</td>\n",
       "      <td>93.86</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>31.286667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>34</td>\n",
       "      <td>81.47</td>\n",
       "      <td>168.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>16.294000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>39</td>\n",
       "      <td>76.73</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25.576667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>30</td>\n",
       "      <td>75.85</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>18.962500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2969</th>\n",
       "      <td>11</td>\n",
       "      <td>78.53</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19.632500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2970</th>\n",
       "      <td>16</td>\n",
       "      <td>113.02</td>\n",
       "      <td>256.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>22.604000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>26</td>\n",
       "      <td>124.54</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>20.756667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2972</th>\n",
       "      <td>11</td>\n",
       "      <td>273.91</td>\n",
       "      <td>700.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>45.651667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2974</th>\n",
       "      <td>6</td>\n",
       "      <td>82.30</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20.575000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>26</td>\n",
       "      <td>135.00</td>\n",
       "      <td>248.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>21</td>\n",
       "      <td>80.11</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>16.022000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>32</td>\n",
       "      <td>88.21</td>\n",
       "      <td>205.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>22.052500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2978</th>\n",
       "      <td>21</td>\n",
       "      <td>68.37</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>17.092500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979</th>\n",
       "      <td>34</td>\n",
       "      <td>178.76</td>\n",
       "      <td>270.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>29.793333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2980</th>\n",
       "      <td>30</td>\n",
       "      <td>86.97</td>\n",
       "      <td>144.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>28.990000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2981</th>\n",
       "      <td>26</td>\n",
       "      <td>92.41</td>\n",
       "      <td>157.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>18.482000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>6</td>\n",
       "      <td>90.43</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30.143333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>7</td>\n",
       "      <td>56.09</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18.696667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>7</td>\n",
       "      <td>131.58</td>\n",
       "      <td>180.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>26.316000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2985</th>\n",
       "      <td>12</td>\n",
       "      <td>63.98</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>21.326667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>18</td>\n",
       "      <td>116.76</td>\n",
       "      <td>152.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23.352000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>11</td>\n",
       "      <td>147.21</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>24.535000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>31</td>\n",
       "      <td>140.88</td>\n",
       "      <td>355.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>23.480000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989</th>\n",
       "      <td>29</td>\n",
       "      <td>108.00</td>\n",
       "      <td>225.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>9</td>\n",
       "      <td>76.00</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2991</th>\n",
       "      <td>33</td>\n",
       "      <td>97.36</td>\n",
       "      <td>182.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>19.472000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>6</td>\n",
       "      <td>157.13</td>\n",
       "      <td>230.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>26.188333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>18</td>\n",
       "      <td>85.23</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>21.307500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>28</td>\n",
       "      <td>69.29</td>\n",
       "      <td>125.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>23.096667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>7</td>\n",
       "      <td>83.84</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20.960000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>32</td>\n",
       "      <td>89.84</td>\n",
       "      <td>199.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>17.968000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>33</td>\n",
       "      <td>90.04</td>\n",
       "      <td>158.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>18.008000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2784 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Floor    Size   Price  Layout_room  Layout_hall  Layout_total  \\\n",
       "1        34   76.48   140.0            2            1             3   \n",
       "2         6   65.49    72.0            2            1             3   \n",
       "3        18  114.48   130.0            3            1             4   \n",
       "4        33   80.54   179.0            2            1             3   \n",
       "5        16  121.92   179.0            3            2             5   \n",
       "6        15   67.28   117.0            2            1             3   \n",
       "7         6  189.38   235.0            4            2             6   \n",
       "8         7  122.79   216.0            3            2             5   \n",
       "9        34   84.77   115.0            3            1             4   \n",
       "10       30  254.22  1100.0            5            2             7   \n",
       "11       21   62.91    70.0            2            1             3   \n",
       "12       11  119.02   195.0            3            2             5   \n",
       "13       31   76.49   200.0            2            2             4   \n",
       "14        7  122.49   232.0            3            2             5   \n",
       "15       18  125.82   190.0            3            2             5   \n",
       "16       33   48.13    60.0            2            2             4   \n",
       "17       21   50.08    90.0            1            1             2   \n",
       "18       28   83.60   240.0            3            1             4   \n",
       "19        6  137.64   203.0            4            3             7   \n",
       "20        7  129.42   117.0            3            2             5   \n",
       "21       34   86.85   157.0            2            2             4   \n",
       "22       22  186.17   370.0            4            2             6   \n",
       "23       26  143.03   278.0            4            2             6   \n",
       "24        7   92.30   120.0            2            2             4   \n",
       "25        6  130.00   200.0            3            1             4   \n",
       "26       18  116.20   225.0            3            2             5   \n",
       "27       31   89.05   249.0            2            2             4   \n",
       "28       25  115.73   200.0            4            2             6   \n",
       "29       25   93.86    93.0            2            1             3   \n",
       "30       34   81.47   168.0            3            2             5   \n",
       "...     ...     ...     ...          ...          ...           ...   \n",
       "2967     39   76.73   155.0            2            1             3   \n",
       "2968     30   75.85   155.0            2            2             4   \n",
       "2969     11   78.53    92.0            2            2             4   \n",
       "2970     16  113.02   256.0            3            2             5   \n",
       "2971     26  124.54   170.0            4            2             6   \n",
       "2972     11  273.91   700.0            4            2             6   \n",
       "2974      6   82.30   110.0            3            1             4   \n",
       "2975     26  135.00   248.0            3            2             5   \n",
       "2976     21   80.11   165.0            3            2             5   \n",
       "2977     32   88.21   205.0            2            2             4   \n",
       "2978     21   68.37   110.0            2            2             4   \n",
       "2979     34  178.76   270.0            4            2             6   \n",
       "2980     30   86.97   144.0            2            1             3   \n",
       "2981     26   92.41   157.0            3            2             5   \n",
       "2982      6   90.43    92.0            2            1             3   \n",
       "2983      7   56.09    69.0            2            1             3   \n",
       "2984      7  131.58   180.0            3            2             5   \n",
       "2985     12   63.98   102.0            2            1             3   \n",
       "2986     18  116.76   152.0            3            2             5   \n",
       "2987     11  147.21   200.0            4            2             6   \n",
       "2988     31  140.88   355.0            4            2             6   \n",
       "2989     29  108.00   225.0            3            1             4   \n",
       "2990      9   76.00   128.0            2            1             3   \n",
       "2991     33   97.36   182.0            3            2             5   \n",
       "2992      6  157.13   230.0            4            2             6   \n",
       "2993     18   85.23   110.0            2            2             4   \n",
       "2994     28   69.29   125.0            2            1             3   \n",
       "2995      7   83.84    92.0            3            1             4   \n",
       "2996     32   89.84   199.0            3            2             5   \n",
       "2997     33   90.04   158.0            3            2             5   \n",
       "\n",
       "      Size_room_ratio  Region_东  Region_东 东北  Region_东 东南      ...        \\\n",
       "1           25.493333         1            0            0      ...         \n",
       "2           21.830000         0            0            0      ...         \n",
       "3           28.620000         0            0            0      ...         \n",
       "4           26.846667         0            0            0      ...         \n",
       "5           24.384000         0            0            0      ...         \n",
       "6           22.426667         0            0            0      ...         \n",
       "7           31.563333         0            0            0      ...         \n",
       "8           24.558000         0            0            0      ...         \n",
       "9           21.192500         0            0            0      ...         \n",
       "10          36.317143         0            0            0      ...         \n",
       "11          20.970000         0            0            0      ...         \n",
       "12          23.804000         0            0            0      ...         \n",
       "13          19.122500         0            0            0      ...         \n",
       "14          24.498000         1            0            0      ...         \n",
       "15          25.164000         0            0            0      ...         \n",
       "16          12.032500         0            0            0      ...         \n",
       "17          25.040000         1            0            0      ...         \n",
       "18          20.900000         0            0            0      ...         \n",
       "19          19.662857         0            0            0      ...         \n",
       "20          25.884000         0            0            0      ...         \n",
       "21          21.712500         0            0            0      ...         \n",
       "22          31.028333         0            0            0      ...         \n",
       "23          23.838333         0            0            0      ...         \n",
       "24          23.075000         0            0            0      ...         \n",
       "25          32.500000         0            0            0      ...         \n",
       "26          23.240000         1            0            0      ...         \n",
       "27          22.262500         1            0            0      ...         \n",
       "28          19.288333         0            0            0      ...         \n",
       "29          31.286667         0            0            0      ...         \n",
       "30          16.294000         0            0            0      ...         \n",
       "...               ...       ...          ...          ...      ...         \n",
       "2967        25.576667         0            0            0      ...         \n",
       "2968        18.962500         0            0            0      ...         \n",
       "2969        19.632500         0            0            0      ...         \n",
       "2970        22.604000         1            0            0      ...         \n",
       "2971        20.756667         0            0            0      ...         \n",
       "2972        45.651667         0            0            0      ...         \n",
       "2974        20.575000         0            0            0      ...         \n",
       "2975        27.000000         0            0            0      ...         \n",
       "2976        16.022000         0            0            0      ...         \n",
       "2977        22.052500         0            0            0      ...         \n",
       "2978        17.092500         1            0            0      ...         \n",
       "2979        29.793333         0            0            0      ...         \n",
       "2980        28.990000         0            0            0      ...         \n",
       "2981        18.482000         0            0            0      ...         \n",
       "2982        30.143333         0            0            0      ...         \n",
       "2983        18.696667         0            0            0      ...         \n",
       "2984        26.316000         0            0            0      ...         \n",
       "2985        21.326667         0            0            0      ...         \n",
       "2986        23.352000         0            1            0      ...         \n",
       "2987        24.535000         0            0            0      ...         \n",
       "2988        23.480000         0            0            0      ...         \n",
       "2989        27.000000         1            0            0      ...         \n",
       "2990        25.333333         0            0            0      ...         \n",
       "2991        19.472000         1            0            0      ...         \n",
       "2992        26.188333         0            0            0      ...         \n",
       "2993        21.307500         0            0            0      ...         \n",
       "2994        23.096667         0            0            0      ...         \n",
       "2995        20.960000         0            0            0      ...         \n",
       "2996        17.968000         0            0            0      ...         \n",
       "2997        18.008000         0            0            0      ...         \n",
       "\n",
       "      Year_(2010.0, 2012.0]  Year_(2012.0, 2013.0]  Year_(2013.0, 2014.0]  \\\n",
       "1                         0                      0                      0   \n",
       "2                         0                      0                      0   \n",
       "3                         1                      0                      0   \n",
       "4                         1                      0                      0   \n",
       "5                         1                      0                      0   \n",
       "6                         0                      0                      0   \n",
       "7                         0                      0                      0   \n",
       "8                         0                      0                      0   \n",
       "9                         0                      0                      1   \n",
       "10                        0                      1                      0   \n",
       "11                        0                      0                      0   \n",
       "12                        0                      0                      0   \n",
       "13                        0                      0                      1   \n",
       "14                        0                      0                      0   \n",
       "15                        0                      0                      0   \n",
       "16                        0                      1                      0   \n",
       "17                        0                      0                      0   \n",
       "18                        1                      0                      0   \n",
       "19                        0                      0                      0   \n",
       "20                        0                      0                      0   \n",
       "21                        0                      0                      0   \n",
       "22                        0                      0                      0   \n",
       "23                        0                      1                      0   \n",
       "24                        0                      0                      0   \n",
       "25                        0                      0                      0   \n",
       "26                        0                      0                      0   \n",
       "27                        0                      0                      1   \n",
       "28                        1                      0                      0   \n",
       "29                        0                      0                      0   \n",
       "30                        1                      0                      0   \n",
       "...                     ...                    ...                    ...   \n",
       "2967                      1                      0                      0   \n",
       "2968                      0                      0                      0   \n",
       "2969                      0                      0                      0   \n",
       "2970                      1                      0                      0   \n",
       "2971                      0                      0                      0   \n",
       "2972                      0                      0                      0   \n",
       "2974                      0                      0                      0   \n",
       "2975                      0                      0                      0   \n",
       "2976                      0                      1                      0   \n",
       "2977                      0                      0                      0   \n",
       "2978                      0                      0                      0   \n",
       "2979                      0                      0                      0   \n",
       "2980                      0                      1                      0   \n",
       "2981                      1                      0                      0   \n",
       "2982                      0                      0                      0   \n",
       "2983                      0                      0                      0   \n",
       "2984                      0                      0                      0   \n",
       "2985                      0                      0                      0   \n",
       "2986                      0                      0                      0   \n",
       "2987                      0                      0                      0   \n",
       "2988                      1                      0                      0   \n",
       "2989                      0                      0                      1   \n",
       "2990                      0                      0                      0   \n",
       "2991                      0                      0                      0   \n",
       "2992                      0                      0                      0   \n",
       "2993                      1                      0                      0   \n",
       "2994                      0                      0                      0   \n",
       "2995                      0                      0                      0   \n",
       "2996                      0                      0                      0   \n",
       "2997                      0                      0                      0   \n",
       "\n",
       "      Year_(2014.0, 2018.0]  Elevator_无  Elevator_有  Renovation_其他  \\\n",
       "1                         0           0           1              1   \n",
       "2                         0           1           0              0   \n",
       "3                         0           0           1              1   \n",
       "4                         0           0           1              0   \n",
       "5                         0           0           1              0   \n",
       "6                         0           0           1              0   \n",
       "7                         0           1           0              1   \n",
       "8                         0           1           0              1   \n",
       "9                         0           0           1              0   \n",
       "10                        0           0           1              1   \n",
       "11                        0           0           1              1   \n",
       "12                        0           0           1              1   \n",
       "13                        0           0           1              1   \n",
       "14                        0           1           0              0   \n",
       "15                        0           0           1              1   \n",
       "16                        0           0           1              0   \n",
       "17                        1           0           1              1   \n",
       "18                        0           0           1              1   \n",
       "19                        0           1           0              1   \n",
       "20                        0           0           1              1   \n",
       "21                        0           0           1              1   \n",
       "22                        0           0           1              1   \n",
       "23                        0           0           1              1   \n",
       "24                        0           1           0              1   \n",
       "25                        0           1           0              0   \n",
       "26                        0           0           1              0   \n",
       "27                        0           0           1              0   \n",
       "28                        0           0           1              1   \n",
       "29                        1           0           1              1   \n",
       "30                        0           0           1              1   \n",
       "...                     ...         ...         ...            ...   \n",
       "2967                      0           0           1              1   \n",
       "2968                      0           0           1              1   \n",
       "2969                      0           0           1              0   \n",
       "2970                      0           0           1              0   \n",
       "2971                      0           0           1              1   \n",
       "2972                      0           0           1              0   \n",
       "2974                      0           1           0              0   \n",
       "2975                      0           0           1              0   \n",
       "2976                      0           0           1              1   \n",
       "2977                      0           0           1              0   \n",
       "2978                      0           0           1              1   \n",
       "2979                      0           0           1              1   \n",
       "2980                      0           0           1              0   \n",
       "2981                      0           0           1              0   \n",
       "2982                      0           1           0              0   \n",
       "2983                      0           1           0              1   \n",
       "2984                      0           1           0              1   \n",
       "2985                      0           0           1              1   \n",
       "2986                      0           0           1              0   \n",
       "2987                      0           0           1              0   \n",
       "2988                      0           0           1              1   \n",
       "2989                      0           0           1              1   \n",
       "2990                      0           0           1              0   \n",
       "2991                      0           0           1              1   \n",
       "2992                      0           1           0              1   \n",
       "2993                      0           0           1              1   \n",
       "2994                      0           0           1              0   \n",
       "2995                      0           1           0              0   \n",
       "2996                      0           0           1              0   \n",
       "2997                      0           0           1              0   \n",
       "\n",
       "      Renovation_毛坯  Renovation_简装  Renovation_精装  \n",
       "1                 0              0              0  \n",
       "2                 0              1              0  \n",
       "3                 0              0              0  \n",
       "4                 0              1              0  \n",
       "5                 0              0              1  \n",
       "6                 0              1              0  \n",
       "7                 0              0              0  \n",
       "8                 0              0              0  \n",
       "9                 1              0              0  \n",
       "10                0              0              0  \n",
       "11                0              0              0  \n",
       "12                0              0              0  \n",
       "13                0              0              0  \n",
       "14                0              1              0  \n",
       "15                0              0              0  \n",
       "16                0              1              0  \n",
       "17                0              0              0  \n",
       "18                0              0              0  \n",
       "19                0              0              0  \n",
       "20                0              0              0  \n",
       "21                0              0              0  \n",
       "22                0              0              0  \n",
       "23                0              0              0  \n",
       "24                0              0              0  \n",
       "25                1              0              0  \n",
       "26                0              0              1  \n",
       "27                0              0              1  \n",
       "28                0              0              0  \n",
       "29                0              0              0  \n",
       "30                0              0              0  \n",
       "...             ...            ...            ...  \n",
       "2967              0              0              0  \n",
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       "2990              0              1              0  \n",
       "2991              0              0              0  \n",
       "2992              0              0              0  \n",
       "2993              0              0              0  \n",
       "2994              0              1              0  \n",
       "2995              1              0              0  \n",
       "2996              0              1              0  \n",
       "2997              0              0              1  \n",
       "\n",
       "[2784 rows x 201 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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